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<h2><a class="anchor" aria-hidden="true" id="use-cases-for-batch-modes"></a><a href="#use-cases-for-batch-modes" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Use cases for batch modes</h2>
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<p>The need for different mesh batch modes is inherent to the way pytorch operators are implemented. To fully utilize the optimized pytorch ops, the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py">Meshes</a> data structure allows for efficient conversion between the different batch modes. This is crucial when aiming for a fast and efficient training cycle. An example of this is <a href="https://github.com/facebookresearch/meshrcnn">Mesh R-CNN</a>. Here, in the same forward pass different parts of the network assume different inputs, which are computed by converting between the different batch modes. In particular, <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/vert_align.py">vert_align</a> assumes a <em>padded</em> input tensor while immediately after <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/graph_conv.py">graph_conv</a> assumes a <em>packed</em> input tensor.</p>
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<p><img src="assets/meshrcnn.png" alt="meshrcnn" width="700" align="middle" /></p>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/why_pytorch3d"><span class="arrow-prev">← </span><span class="function-name-prevnext">Why PyTorch3D</span></a><a class="docs-next button" href="/docs/meshes_io"><span>Loading from file</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#batch-modes-for-meshes">Batch modes for meshes</a></li><li><a href="#use-cases-for-batch-modes">Use cases for batch modes</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/datasets"><span class="arrow-prev">← </span><span>Data loaders</span></a><a class="docs-next button" href="/docs/cubify"><span>Cubify</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#batch-modes-for-meshes">Batch modes for meshes</a></li><li><a href="#use-cases-for-batch-modes">Use cases for batch modes</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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<h2><a class="anchor" aria-hidden="true" id="use-cases-for-batch-modes"></a><a href="#use-cases-for-batch-modes" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Use cases for batch modes</h2>
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<p>The need for different mesh batch modes is inherent to the way pytorch operators are implemented. To fully utilize the optimized pytorch ops, the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py">Meshes</a> data structure allows for efficient conversion between the different batch modes. This is crucial when aiming for a fast and efficient training cycle. An example of this is <a href="https://github.com/facebookresearch/meshrcnn">Mesh R-CNN</a>. Here, in the same forward pass different parts of the network assume different inputs, which are computed by converting between the different batch modes. In particular, <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/vert_align.py">vert_align</a> assumes a <em>padded</em> input tensor while immediately after <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/graph_conv.py">graph_conv</a> assumes a <em>packed</em> input tensor.</p>
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<p><img src="assets/meshrcnn.png" alt="meshrcnn" width="700" align="middle" /></p>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/why_pytorch3d"><span class="arrow-prev">← </span><span class="function-name-prevnext">Why PyTorch3D</span></a><a class="docs-next button" href="/docs/meshes_io"><span>Loading from file</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#batch-modes-for-meshes">Batch modes for meshes</a></li><li><a href="#use-cases-for-batch-modes">Use cases for batch modes</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="cameras"></a><a href="#cameras" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cameras</h1>
|
||||
<h2><a class="anchor" aria-hidden="true" id="camera-coordinate-systems"></a><a href="#camera-coordinate-systems" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Camera Coordinate Systems</h2>
|
||||
<p>When working with 3D data, there are 4 coordinate systems users need to know</p>
|
||||
<ul>
|
||||
<li><strong>World coordinate system</strong>
|
||||
This is the system the object/scene lives - the world.</li>
|
||||
<li><strong>Camera view coordinate system</strong>
|
||||
This is the system that has its origin on the image plane and the <code>Z</code>-axis perpendicular to the image plane. In PyTorch3D, we assume that <code>+X</code> points left, and <code>+Y</code> points up and <code>+Z</code> points out from the image plane. The transformation from world to view happens after applying a rotation (<code>R</code>) and translation (<code>T</code>).</li>
|
||||
<li><strong>NDC coordinate system</strong>
|
||||
This is the normalized coordinate system that confines in a volume the renderered part of the object/scene. Also known as view volume. Under the PyTorch3D convention, <code>(+1, +1, znear)</code> is the top left near corner, and <code>(-1, -1, zfar)</code> is the bottom right far corner of the volume. The transformation from view to NDC happens after applying the camera projection matrix (<code>P</code>).</li>
|
||||
<li><strong>Screen coordinate system</strong>
|
||||
This is another representation of the view volume with the <code>XY</code> coordinates defined in pixel space instead of a normalized space.</li>
|
||||
</ul>
|
||||
<p>An illustration of the 4 coordinate systems is shown below
|
||||
<img src="https://user-images.githubusercontent.com/4369065/90317960-d9b8db80-dee1-11ea-8088-39c414b1e2fa.png" alt="cameras"></p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="defining-cameras-in-pytorch3d"></a><a href="#defining-cameras-in-pytorch3d" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Defining Cameras in PyTorch3D</h2>
|
||||
<p>Cameras in PyTorch3D transform an object/scene from world to NDC by first transforming the object/scene to view (via transforms <code>R</code> and <code>T</code>) and then projecting the 3D object/scene to NDC (via the projection matrix <code>P</code>, else known as camera matrix). Thus, the camera parameters in <code>P</code> are assumed to be in NDC space. If the user has camera parameters in screen space, which is a common use case, the parameters should transformed to NDC (see below for an example)</p>
|
||||
<p>We describe the camera types in PyTorch3D and the convention for the camera parameters provided at construction time.</p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="camera-types"></a><a href="#camera-types" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Camera Types</h3>
|
||||
<p>All cameras inherit from <code>CamerasBase</code> which is a base class for all cameras. PyTorch3D provides four different camera types. The <code>CamerasBase</code> defines methods that are common to all camera models:</p>
|
||||
<ul>
|
||||
<li><code>get_camera_center</code> that returns the optical center of the camera in world coordinates</li>
|
||||
<li><code>get_world_to_view_transform</code> which returns a 3D transform from world coordinates to the camera view coordinates (R, T)</li>
|
||||
<li><code>get_full_projection_transform</code> which composes the projection transform (P) with the world-to-view transform (R, T)</li>
|
||||
<li><code>transform_points</code> which takes a set of input points in world coordinates and projects to NDC coordinates ranging from [-1, -1, znear] to [+1, +1, zfar].</li>
|
||||
<li><code>transform_points_screen</code> which takes a set of input points in world coordinates and projects them to the screen coordinates ranging from [0, 0, znear] to [W-1, H-1, zfar]</li>
|
||||
</ul>
|
||||
<p>Users can easily customize their own cameras. For each new camera, users should implement the <code>get_projection_transform</code> routine that returns the mapping <code>P</code> from camera view coordinates to NDC coordinates.</p>
|
||||
<h4><a class="anchor" aria-hidden="true" id="fovperspectivecameras-fovorthographiccameras"></a><a href="#fovperspectivecameras-fovorthographiccameras" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>FoVPerspectiveCameras, FoVOrthographicCameras</h4>
|
||||
<p>These two cameras follow the OpenGL convention for perspective and orthographic cameras respectively. The user provides the near <code>znear</code> and far <code>zfar</code> field which confines the view volume in the <code>Z</code> axis. The view volume in the <code>XY</code> plane is defined by field of view angle (<code>fov</code>) in the case of <code>FoVPerspectiveCameras</code> and by <code>min_x, min_y, max_x, max_y</code> in the case of <code>FoVOrthographicCameras</code>.</p>
|
||||
<h4><a class="anchor" aria-hidden="true" id="perspectivecameras-orthographiccameras"></a><a href="#perspectivecameras-orthographiccameras" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>PerspectiveCameras, OrthographicCameras</h4>
|
||||
<p>These two cameras follow the Multi-View Geometry convention for cameras. The user provides the focal length (<code>fx</code>, <code>fy</code>) and the principal point (<code>px</code>, <code>py</code>). For example, <code>camera = PerspectiveCameras(focal_length=((fx, fy),), principal_point=((px, py),))</code></p>
|
||||
<p>As mentioned above, the focal length and principal point are used to convert a point <code>(X, Y, Z)</code> from view coordinates to NDC coordinates, as follows</p>
|
||||
<pre><code class="hljs"><span class="hljs-comment"># for perspective</span>
|
||||
<span class="hljs-attr">x_ndc</span> = fx * X / Z + px
|
||||
<span class="hljs-attr">y_ndc</span> = fy * Y / Z + py
|
||||
<span class="hljs-attr">z_ndc</span> = <span class="hljs-number">1</span> / Z
|
||||
|
||||
<span class="hljs-comment"># for orthographic</span>
|
||||
<span class="hljs-attr">x_ndc</span> = fx * X + px
|
||||
<span class="hljs-attr">y_ndc</span> = fy * Y + py
|
||||
<span class="hljs-attr">z_ndc</span> = Z
|
||||
</code></pre>
|
||||
<p>Commonly, users have access to the focal length (<code>fx_screen</code>, <code>fy_screen</code>) and the principal point (<code>px_screen</code>, <code>py_screen</code>) in screen space. In that case, to construct the camera the user needs to additionally provide the <code>image_size = ((image_width, image_height),)</code>. More precisely, <code>camera = PerspectiveCameras(focal_length=((fx_screen, fy_screen),), principal_point=((px_screen, py_screen),), image_size = ((image_width, image_height),))</code>. Internally, the camera parameters are converted from screen to NDC as follows:</p>
|
||||
<pre><code class="hljs"><span class="hljs-attr">fx</span> = fx_screen * <span class="hljs-number">2.0</span> / image_width
|
||||
<span class="hljs-attr">fy</span> = fy_screen * <span class="hljs-number">2.0</span> / image_height
|
||||
|
||||
<span class="hljs-attr">px</span> = - (px_screen - image_width / <span class="hljs-number">2.0</span>) * <span class="hljs-number">2.0</span> / image_width
|
||||
<span class="hljs-attr">py</span> = - (py_screen - image_height / <span class="hljs-number">2.0</span>) * <span class="hljs-number">2.0</span>/ image_height
|
||||
</code></pre>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/renderer_getting_started"><span class="arrow-prev">← </span><span>Getting Started</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#camera-coordinate-systems">Camera Coordinate Systems</a></li><li><a href="#defining-cameras-in-pytorch3d">Defining Cameras in PyTorch3D</a><ul class="toc-headings"><li><a href="#camera-types">Camera Types</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="cameras"></a><a href="#cameras" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cameras</h1>
|
||||
<h2><a class="anchor" aria-hidden="true" id="camera-coordinate-systems"></a><a href="#camera-coordinate-systems" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Camera Coordinate Systems</h2>
|
||||
<p>When working with 3D data, there are 4 coordinate systems users need to know</p>
|
||||
<ul>
|
||||
<li><strong>World coordinate system</strong>
|
||||
This is the system the object/scene lives - the world.</li>
|
||||
<li><strong>Camera view coordinate system</strong>
|
||||
This is the system that has its origin on the image plane and the <code>Z</code>-axis perpendicular to the image plane. In PyTorch3D, we assume that <code>+X</code> points left, and <code>+Y</code> points up and <code>+Z</code> points out from the image plane. The transformation from world to view happens after applying a rotation (<code>R</code>) and translation (<code>T</code>).</li>
|
||||
<li><strong>NDC coordinate system</strong>
|
||||
This is the normalized coordinate system that confines in a volume the renderered part of the object/scene. Also known as view volume. Under the PyTorch3D convention, <code>(+1, +1, znear)</code> is the top left near corner, and <code>(-1, -1, zfar)</code> is the bottom right far corner of the volume. The transformation from view to NDC happens after applying the camera projection matrix (<code>P</code>).</li>
|
||||
<li><strong>Screen coordinate system</strong>
|
||||
This is another representation of the view volume with the <code>XY</code> coordinates defined in pixel space instead of a normalized space.</li>
|
||||
</ul>
|
||||
<p>An illustration of the 4 coordinate systems is shown below
|
||||
<img src="https://user-images.githubusercontent.com/4369065/90317960-d9b8db80-dee1-11ea-8088-39c414b1e2fa.png" alt="cameras"></p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="defining-cameras-in-pytorch3d"></a><a href="#defining-cameras-in-pytorch3d" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Defining Cameras in PyTorch3D</h2>
|
||||
<p>Cameras in PyTorch3D transform an object/scene from world to NDC by first transforming the object/scene to view (via transforms <code>R</code> and <code>T</code>) and then projecting the 3D object/scene to NDC (via the projection matrix <code>P</code>, else known as camera matrix). Thus, the camera parameters in <code>P</code> are assumed to be in NDC space. If the user has camera parameters in screen space, which is a common use case, the parameters should transformed to NDC (see below for an example)</p>
|
||||
<p>We describe the camera types in PyTorch3D and the convention for the camera parameters provided at construction time.</p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="camera-types"></a><a href="#camera-types" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Camera Types</h3>
|
||||
<p>All cameras inherit from <code>CamerasBase</code> which is a base class for all cameras. PyTorch3D provides four different camera types. The <code>CamerasBase</code> defines methods that are common to all camera models:</p>
|
||||
<ul>
|
||||
<li><code>get_camera_center</code> that returns the optical center of the camera in world coordinates</li>
|
||||
<li><code>get_world_to_view_transform</code> which returns a 3D transform from world coordinates to the camera view coordinates (R, T)</li>
|
||||
<li><code>get_full_projection_transform</code> which composes the projection transform (P) with the world-to-view transform (R, T)</li>
|
||||
<li><code>transform_points</code> which takes a set of input points in world coordinates and projects to NDC coordinates ranging from [-1, -1, znear] to [+1, +1, zfar].</li>
|
||||
<li><code>transform_points_screen</code> which takes a set of input points in world coordinates and projects them to the screen coordinates ranging from [0, 0, znear] to [W-1, H-1, zfar]</li>
|
||||
</ul>
|
||||
<p>Users can easily customize their own cameras. For each new camera, users should implement the <code>get_projection_transform</code> routine that returns the mapping <code>P</code> from camera view coordinates to NDC coordinates.</p>
|
||||
<h4><a class="anchor" aria-hidden="true" id="fovperspectivecameras-fovorthographiccameras"></a><a href="#fovperspectivecameras-fovorthographiccameras" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>FoVPerspectiveCameras, FoVOrthographicCameras</h4>
|
||||
<p>These two cameras follow the OpenGL convention for perspective and orthographic cameras respectively. The user provides the near <code>znear</code> and far <code>zfar</code> field which confines the view volume in the <code>Z</code> axis. The view volume in the <code>XY</code> plane is defined by field of view angle (<code>fov</code>) in the case of <code>FoVPerspectiveCameras</code> and by <code>min_x, min_y, max_x, max_y</code> in the case of <code>FoVOrthographicCameras</code>.</p>
|
||||
<h4><a class="anchor" aria-hidden="true" id="perspectivecameras-orthographiccameras"></a><a href="#perspectivecameras-orthographiccameras" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>PerspectiveCameras, OrthographicCameras</h4>
|
||||
<p>These two cameras follow the Multi-View Geometry convention for cameras. The user provides the focal length (<code>fx</code>, <code>fy</code>) and the principal point (<code>px</code>, <code>py</code>). For example, <code>camera = PerspectiveCameras(focal_length=((fx, fy),), principal_point=((px, py),))</code></p>
|
||||
<p>As mentioned above, the focal length and principal point are used to convert a point <code>(X, Y, Z)</code> from view coordinates to NDC coordinates, as follows</p>
|
||||
<pre><code class="hljs"><span class="hljs-comment"># for perspective</span>
|
||||
<span class="hljs-attr">x_ndc</span> = fx * X / Z + px
|
||||
<span class="hljs-attr">y_ndc</span> = fy * Y / Z + py
|
||||
<span class="hljs-attr">z_ndc</span> = <span class="hljs-number">1</span> / Z
|
||||
|
||||
<span class="hljs-comment"># for orthographic</span>
|
||||
<span class="hljs-attr">x_ndc</span> = fx * X + px
|
||||
<span class="hljs-attr">y_ndc</span> = fy * Y + py
|
||||
<span class="hljs-attr">z_ndc</span> = Z
|
||||
</code></pre>
|
||||
<p>Commonly, users have access to the focal length (<code>fx_screen</code>, <code>fy_screen</code>) and the principal point (<code>px_screen</code>, <code>py_screen</code>) in screen space. In that case, to construct the camera the user needs to additionally provide the <code>image_size = ((image_width, image_height),)</code>. More precisely, <code>camera = PerspectiveCameras(focal_length=((fx_screen, fy_screen),), principal_point=((px_screen, py_screen),), image_size = ((image_width, image_height),))</code>. Internally, the camera parameters are converted from screen to NDC as follows:</p>
|
||||
<pre><code class="hljs"><span class="hljs-attr">fx</span> = fx_screen * <span class="hljs-number">2.0</span> / image_width
|
||||
<span class="hljs-attr">fy</span> = fy_screen * <span class="hljs-number">2.0</span> / image_height
|
||||
|
||||
<span class="hljs-attr">px</span> = - (px_screen - image_width / <span class="hljs-number">2.0</span>) * <span class="hljs-number">2.0</span> / image_width
|
||||
<span class="hljs-attr">py</span> = - (py_screen - image_height / <span class="hljs-number">2.0</span>) * <span class="hljs-number">2.0</span>/ image_height
|
||||
</code></pre>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/renderer_getting_started"><span class="arrow-prev">← </span><span>Getting Started</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#camera-coordinate-systems">Camera Coordinate Systems</a></li><li><a href="#defining-cameras-in-pytorch3d">Defining Cameras in PyTorch3D</a><ul class="toc-headings"><li><a href="#camera-types">Camera Types</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="cubify"></a><a href="#cubify" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cubify</h1>
|
||||
<p>The <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/cubify.py">cubify operator</a> converts an 3D occupancy grid of shape <code>BxDxHxW</code>, where <code>B</code> is the batch size, into a mesh instantiated as a <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py">Meshes</a> data structure of <code>B</code> elements. The operator replaces every occupied voxel (if its occupancy probability is greater than a user defined threshold) with a cuboid of 12 faces and 8 vertices. Shared vertices are merged, and internal faces are removed resulting in a <strong>watertight</strong> mesh.</p>
|
||||
<p>The operator provides three alignment modes {<em>topleft</em>, <em>corner</em>, <em>center</em>} which define the span of the mesh vertices with respect to the voxel grid. The alignment modes are described in the figure below for a 2D grid.</p>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="cubify"></a><a href="#cubify" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cubify</h1>
|
||||
<p>The <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/cubify.py">cubify operator</a> converts an 3D occupancy grid of shape <code>BxDxHxW</code>, where <code>B</code> is the batch size, into a mesh instantiated as a <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py">Meshes</a> data structure of <code>B</code> elements. The operator replaces every occupied voxel (if its occupancy probability is greater than a user defined threshold) with a cuboid of 12 faces and 8 vertices. Shared vertices are merged, and internal faces are removed resulting in a <strong>watertight</strong> mesh.</p>
|
||||
<p>The operator provides three alignment modes {<em>topleft</em>, <em>corner</em>, <em>center</em>} which define the span of the mesh vertices with respect to the voxel grid. The alignment modes are described in the figure below for a 2D grid.</p>
|
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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>datasets · PyTorch3D</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="# Data loaders for common 3D Datasets"/><meta name="docsearch:language" content="en"/><meta property="og:title" content="datasets · PyTorch3D"/><meta property="og:type" content="website"/><meta property="og:url" content="https://pytorch3d.org/"/><meta property="og:description" content="# Data loaders for common 3D Datasets"/><meta property="og:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><link rel="shortcut icon" href="/img/pytorch3dfavicon.png"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css"/><script>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="data-loaders-for-common-3d-datasets"></a><a href="#data-loaders-for-common-3d-datasets" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data loaders for common 3D Datasets</h1>
|
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<h3><a class="anchor" aria-hidden="true" id="shapetnetcore"></a><a href="#shapetnetcore" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>ShapetNetCore</h3>
|
||||
<p>ShapeNet is a dataset of 3D CAD models. ShapeNetCore is a subset of the ShapeNet dataset and can be downloaded from <a href="https://www.shapenet.org/">https://www.shapenet.org/</a>. There are two versions ShapeNetCore: v1 (55 categories) and v2 (57 categories).</p>
|
||||
<p>The PyTorch3D <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/datasets/shapenet/shapenet_core.py">ShapeNetCore data loader</a> inherits from <code>torch.utils.data.Dataset</code>. It takes the path where the ShapeNetCore dataset is stored locally and loads models in the dataset. The ShapeNetCore class loads and returns models with their <code>categories</code>, <code>model_ids</code>, <code>vertices</code> and <code>faces</code>. The <code>ShapeNetCore</code> data loader also has a customized <code>render</code> function that renders models by the specified <code>model_ids (List[int])</code>, <code>categories (List[str])</code> or <code>indices (List[int])</code> with PyTorch3D's differentiable renderer.</p>
|
||||
<p>The loaded dataset can be passed to <code>torch.utils.data.DataLoader</code> with PyTorch3D's customized collate_fn: <code>collate_batched_meshes</code> from the <code>pytorch3d.dataset.utils</code> module. The <code>vertices</code> and <code>faces</code> of the models are used to construct a <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py">Meshes</a> object representing the batched meshes. This <code>Meshes</code> representation can be easily used with other ops and rendering in PyTorch3D.</p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="r2n2"></a><a href="#r2n2" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>R2N2</h3>
|
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<p>The R2N2 dataset contains 13 categories that are a subset of the ShapeNetCore v.1 dataset. The R2N2 dataset also contains its own 24 renderings of each object and voxelized models. The R2N2 Dataset can be downloaded following the instructions <a href="http://3d-r2n2.stanford.edu/">here</a>.</p>
|
||||
<p>The PyTorch3D <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/datasets/r2n2/r2n2.py">R2N2 data loader</a> is initialized with the paths to the ShapeNet dataset, the R2N2 dataset and the splits file for R2N2. Just like <code>ShapeNetCore</code>, it can be passed to <code>torch.utils.data.DataLoader</code> with a customized collate_fn: <code>collate_batched_R2N2</code> from the <code>pytorch3d.dataset.r2n2.utils</code> module. It returns all the data that <code>ShapeNetCore</code> returns, and in addition, it returns the R2N2 renderings (24 views for each model) along with the camera calibration matrices and a voxel representation for each model. Similar to <code>ShapeNetCore</code>, it has a customized <code>render</code> function that supports rendering specified models with the PyTorch3D differentiable renderer. In addition, it supports rendering models with the same orientations as R2N2's original renderings.</p>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="data-loaders-for-common-3d-datasets"></a><a href="#data-loaders-for-common-3d-datasets" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data loaders for common 3D Datasets</h1>
|
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<h3><a class="anchor" aria-hidden="true" id="shapetnetcore"></a><a href="#shapetnetcore" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>ShapetNetCore</h3>
|
||||
<p>ShapeNet is a dataset of 3D CAD models. ShapeNetCore is a subset of the ShapeNet dataset and can be downloaded from <a href="https://www.shapenet.org/">https://www.shapenet.org/</a>. There are two versions ShapeNetCore: v1 (55 categories) and v2 (57 categories).</p>
|
||||
<p>The PyTorch3D <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/datasets/shapenet/shapenet_core.py">ShapeNetCore data loader</a> inherits from <code>torch.utils.data.Dataset</code>. It takes the path where the ShapeNetCore dataset is stored locally and loads models in the dataset. The ShapeNetCore class loads and returns models with their <code>categories</code>, <code>model_ids</code>, <code>vertices</code> and <code>faces</code>. The <code>ShapeNetCore</code> data loader also has a customized <code>render</code> function that renders models by the specified <code>model_ids (List[int])</code>, <code>categories (List[str])</code> or <code>indices (List[int])</code> with PyTorch3D's differentiable renderer.</p>
|
||||
<p>The loaded dataset can be passed to <code>torch.utils.data.DataLoader</code> with PyTorch3D's customized collate_fn: <code>collate_batched_meshes</code> from the <code>pytorch3d.dataset.utils</code> module. The <code>vertices</code> and <code>faces</code> of the models are used to construct a <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py">Meshes</a> object representing the batched meshes. This <code>Meshes</code> representation can be easily used with other ops and rendering in PyTorch3D.</p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="r2n2"></a><a href="#r2n2" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>R2N2</h3>
|
||||
<p>The R2N2 dataset contains 13 categories that are a subset of the ShapeNetCore v.1 dataset. The R2N2 dataset also contains its own 24 renderings of each object and voxelized models. The R2N2 Dataset can be downloaded following the instructions <a href="http://3d-r2n2.stanford.edu/">here</a>.</p>
|
||||
<p>The PyTorch3D <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/datasets/r2n2/r2n2.py">R2N2 data loader</a> is initialized with the paths to the ShapeNet dataset, the R2N2 dataset and the splits file for R2N2. Just like <code>ShapeNetCore</code>, it can be passed to <code>torch.utils.data.DataLoader</code> with a customized collate_fn: <code>collate_batched_R2N2</code> from the <code>pytorch3d.dataset.r2n2.utils</code> module. It returns all the data that <code>ShapeNetCore</code> returns, and in addition, it returns the R2N2 renderings (24 views for each model) along with the camera calibration matrices and a voxel representation for each model. Similar to <code>ShapeNetCore</code>, it has a customized <code>render</code> function that supports rendering specified models with the PyTorch3D differentiable renderer. In addition, it supports rendering models with the same orientations as R2N2's original renderings.</p>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/meshes_io"><span class="arrow-prev">← </span><span>Loading from file</span></a><a class="docs-next button" href="/docs/batching"><span>Batching</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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single mesh can be created from this data using</p>
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||||
<pre><code class="hljs"> meshes = <span class="hljs-constructor">Meshes(<span class="hljs-params">verts</span>=[<span class="hljs-params">verts</span>], <span class="hljs-params">faces</span>=[<span class="hljs-params">faces</span>])</span>
|
||||
</code></pre>
|
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Jeremy Reizenstein</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/batching"><span class="arrow-prev">← </span><span>Batching</span></a><a class="docs-next button" href="/docs/renderer"><span>Overview</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#obj">OBJ</a></li><li><a href="#ply">PLY</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</script><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/pytorch3dfavicon.png" alt="PyTorch3D"/><h2 class="headerTitleWithLogo">PyTorch3D</h2></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/why_pytorch3d" target="_self">Docs</a></li><li class=""><a href="/tutorials" target="_self">Tutorials</a></li><li class=""><a href="https://pytorch3d.readthedocs.io/" target="_self">API</a></li><li class=""><a href="https://github.com/facebookresearch/pytorch3d" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Data</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Introduction</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/why_pytorch3d">Why PyTorch3D</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Data</h3><ul class=""><li class="navListItem navListItemActive"><a class="navItem" href="/docs/meshes_io">Loading from file</a></li><li class="navListItem"><a class="navItem" href="/docs/datasets">Data loaders</a></li><li class="navListItem"><a class="navItem" href="/docs/batching">Batching</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Ops</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/cubify">Cubify</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Renderer</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/renderer">Overview</a></li><li class="navListItem"><a class="navItem" href="/docs/renderer_getting_started">Getting Started</a></li><li class="navListItem"><a class="navItem" href="/docs/cameras">Cameras</a></li></ul></div></div></section></div><script>
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@@ -116,4 +116,4 @@ are not triangles will be split into triangles. A Meshes object containing a
|
||||
single mesh can be created from this data using</p>
|
||||
<pre><code class="hljs"> meshes = <span class="hljs-constructor">Meshes(<span class="hljs-params">verts</span>=[<span class="hljs-params">verts</span>], <span class="hljs-params">faces</span>=[<span class="hljs-params">faces</span>])</span>
|
||||
</code></pre>
|
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Jeremy Reizenstein</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/batching"><span class="arrow-prev">← </span><span>Batching</span></a><a class="docs-next button" href="/docs/renderer"><span>Overview</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#obj">OBJ</a></li><li><a href="#ply">PLY</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Jeremy Reizenstein</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/why_pytorch3d"><span class="arrow-prev">← </span><span class="function-name-prevnext">Why PyTorch3D</span></a><a class="docs-next button" href="/docs/datasets"><span>Data loaders</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#obj">OBJ</a></li><li><a href="#ply">PLY</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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ga('create', 'UA-157376881-1', 'auto');
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ga('send', 'pageview');
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</script><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/pytorch3dfavicon.png" alt="PyTorch3D"/><h2 class="headerTitleWithLogo">PyTorch3D</h2></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/why_pytorch3d" target="_self">Docs</a></li><li class=""><a href="/tutorials" target="_self">Tutorials</a></li><li class=""><a href="https://pytorch3d.readthedocs.io/" target="_self">API</a></li><li class=""><a href="https://github.com/facebookresearch/pytorch3d" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Differentiable Renderer</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Introduction</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/why_pytorch3d">Why PyTorch3D</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Meshes</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/batching">Batching</a></li><li class="navListItem"><a class="navItem" href="/docs/meshes_io">Loading from file</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Differentiable Renderer</h3><ul class=""><li class="navListItem navListItemActive"><a class="navItem" href="/docs/renderer">Overview</a></li><li class="navListItem"><a class="navItem" href="/docs/renderer_getting_started">Getting Started</a></li></ul></div></div></section></div><script>
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</script><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/pytorch3dfavicon.png" alt="PyTorch3D"/><h2 class="headerTitleWithLogo">PyTorch3D</h2></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/why_pytorch3d" target="_self">Docs</a></li><li class=""><a href="/tutorials" target="_self">Tutorials</a></li><li class=""><a href="https://pytorch3d.readthedocs.io/" target="_self">API</a></li><li class=""><a href="https://github.com/facebookresearch/pytorch3d" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Renderer</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Introduction</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/why_pytorch3d">Why PyTorch3D</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Data</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/meshes_io">Loading from file</a></li><li class="navListItem"><a class="navItem" href="/docs/datasets">Data loaders</a></li><li class="navListItem"><a class="navItem" href="/docs/batching">Batching</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Ops</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/cubify">Cubify</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Renderer</h3><ul class=""><li class="navListItem navListItemActive"><a class="navItem" href="/docs/renderer">Overview</a></li><li class="navListItem"><a class="navItem" href="/docs/renderer_getting_started">Getting Started</a></li><li class="navListItem"><a class="navItem" href="/docs/cameras">Cameras</a></li></ul></div></div></section></div><script>
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var coll = document.getElementsByClassName('collapsible');
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for (var i = 0; i < coll.length; i++) {
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@@ -72,38 +72,15 @@
|
||||
<li>existing methods either do not support batching or assume that meshes in a batch have the same number of vertices and faces</li>
|
||||
<li>existing projects only provide CUDA implementations so they cannot be used without GPUs</li>
|
||||
</ul>
|
||||
<p>In order to experiment with different approaches, we wanted a modular implementation that is easy to use and extend, and supports <a href="/docs/batching">heterogeneous batching</a>.</p>
|
||||
<p>Taking inspiration from existing work [<a href="#1">1</a>, <a href="#2">2</a>], we have created a new, modular, differentiable renderer with <strong>parallel implementations in PyTorch, C++ and CUDA</strong>, as well as comprehensive documentation and tests, with the aim of helping to further research in this field.</p>
|
||||
<p>In order to experiment with different approaches, we wanted a modular implementation that is easy to use and extend, and supports <a href="/docs/batching">heterogeneous batching</a>. Taking inspiration from existing work [<a href="#1">1</a>, <a href="#2">2</a>], we have created a new, modular, differentiable renderer with <strong>parallel implementations in PyTorch, C++ and CUDA</strong>, as well as comprehensive documentation and tests, with the aim of helping to further research in this field.</p>
|
||||
<p>Our implementation decouples the rasterization and shading steps of rendering. The core rasterization step (based on <a href="#2">[2]</a>) returns several intermediate variables and has an optimized implementation in CUDA. The rest of the pipeline is implemented purely in PyTorch, and is designed to be customized and extended. With this approach, the PyTorch3D differentiable renderer can be imported as a library.</p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="uget-startedu"></a><a href="#uget-startedu" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><u>Get started</u></h2>
|
||||
<p>To learn about more the implementation and start using the renderer refer to <a href="renderer_getting_started.md">/docs/renderer_getting_started</a>, which also contains the <a href="/docs/assets/architecture_overview.png">architecture overview</a> and <a href="/docs/assets/transformations_overview.png">coordinate transformation conventions</a>.</p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="ukey-featuresu"></a><a href="#ukey-featuresu" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><u>Key features</u></h2>
|
||||
<h3><a class="anchor" aria-hidden="true" id="1-cuda-support-for-fast-rasterization-of-large-meshes"></a><a href="#1-cuda-support-for-fast-rasterization-of-large-meshes" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>1. CUDA support for fast rasterization of large meshes</h3>
|
||||
<p>We implemented modular CUDA kernels for the forward and backward pass of rasterization, adaptating a traditional graphics approach known as "coarse-to-fine" rasterization.</p>
|
||||
<p>First, the image is divided into a coarse grid and mesh faces are allocated to the grid cell in which they occur. This is followed by a refinement step which does pixel wise rasterization of the reduced subset of faces per grid cell. The grid cell size is a parameter which can be varied (<code>bin_size</code>).</p>
|
||||
<p>We additionally introduce a parameter <code>faces_per_pixel</code> which allows users to specify the top K faces which should be returned per pixel in the image (as opposed to traditional rasterization which returns only the index of the closest face in the mesh per pixel). The top K face properties can then be aggregated using different methods (such as the sigmoid/softmax approach proposed by Li et at in SoftRasterizer <a href="#2">[2]</a>).</p>
|
||||
<p>We compared PyTorch3D with SoftRasterizer to measure the effect of both these design changes on the speed of rasterization. We selected a set of meshes of different sizes from ShapeNetV1 core, and rasterized one mesh in each batch to produce images of different sizes. We report the speed of the forward and backward passes.</p>
|
||||
<p><strong>Fig 1: PyTorch3D Naive vs Coarse-to-fine</strong></p>
|
||||
<p>This figure shows how the coarse-to-fine strategy for rasterization results in significant speed up compared to naive rasterization for large image size and large mesh sizes.</p>
|
||||
<p><img src="assets/p3d_naive_vs_coarse.png" width="1000"></p>
|
||||
<p>For small mesh and image sizes, the naive approach is slightly faster. We advise that you understand the data you are using and choose the rasterization setting which suits your performance requirements. It is easy to switch between the naive and coarse-to-fine options by adjusting the <code>bin_size</code> value when initializing the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/rasterizer.py#L26">rasterization settings</a>.</p>
|
||||
<p>Setting <code>bin_size = 0</code> will enable naive rasterization. If <code>bin_size > 0</code>, the coarse-to-fine approach is used. The default is <code>bin_size = None</code> in which case we set the bin size based on <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/rasterize_meshes.py#L92">heuristics</a>.</p>
|
||||
<p><strong>Fig 2: PyTorch3D Coarse-to-fine vs SoftRasterizer</strong></p>
|
||||
<p>This figure shows the effect of the <em>combination</em> of coarse-to-fine rasterization and caching the faces rasterized per pixel returned from the forward pass. For large meshes and image sizes, we again observe that the PyTorch3D rasterizer is significantly faster, noting that the speed is dominated by the forward pass and the backward pass is very fast.</p>
|
||||
<p>In the SoftRasterizer implementation, in both the forward and backward pass, there is a loop over every single face in the mesh for every pixel in the image. Therefore, the time for the full forward plus backward pass is ~2x the time for the forward pass. For small mesh and image sizes, the SoftRasterizer approach is slightly faster.</p>
|
||||
<p><img src="assets/p3d_vs_softras.png" width="1000"></p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="2-support-for-heterogeneous-batches"></a><a href="#2-support-for-heterogeneous-batches" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>2. Support for Heterogeneous Batches</h3>
|
||||
<p>PyTorch3D supports efficient rendering of batches of meshes where each mesh has different numbers of vertices and faces. This is done without using padded inputs.</p>
|
||||
<p>We again compare with SoftRasterizer which only supports batches of homogeneous meshes and test two cases: 1) a for loop over meshes in the batch, 2) padded inputs, and compare with the native heterogeneous batching support in PyTorch3D.</p>
|
||||
<p>We group meshes from ShapeNet into bins based on the number of faces in the mesh, and sample to compose a batch. We then render images of fixed size and measure the speed of the forward and backward passes.</p>
|
||||
<p>We tested with a range of increasingly large meshes and bin sizes.</p>
|
||||
<p><strong>Fig 3: PyTorch3D heterogeneous batching compared with SoftRasterizer</strong></p>
|
||||
<p><img src="assets/fullset_batch_size_16.png" width="700"/></p>
|
||||
<p>This shows that for large meshes and large bin width (i.e. more variation in mesh size in the batch) the heterogeneous batching approach in PyTorch3D is faster than either of the workarounds with SoftRasterizer.</p>
|
||||
<p>(settings: batch size = 16, mesh sizes in bins ranging from 500-350k faces, image size = 64, faces per pixel = 100)</p>
|
||||
<p>To learn about more the implementation and start using the renderer refer to <a href="/docs/renderer_getting_started">getting started with renderer</a>, which also contains the <a href="/docs/assets/architecture_overview.png">architecture overview</a> and <a href="/docs/assets/transformations_overview.png">coordinate transformation conventions</a>.</p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="utech-reportu"></a><a href="#utech-reportu" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><u>Tech Report</u></h2>
|
||||
<p>For an in depth explanation of the renderer design, key features and benchmarks please refer to the PyTorch3D Technical Report on ArXiv: <a href="https://arxiv.org/abs/2007.08501">Accelerating 3D Deep Learning with PyTorch3D</a></p>
|
||||
<hr>
|
||||
<p><strong>NOTE: CUDA Memory usage</strong></p>
|
||||
<p>The SoftRasterizer forward CUDA kernel only outputs one <code>(N, H, W, 4)</code> FloatTensor compared with the PyTorch3D rasterizer forward CUDA kernel which outputs 4 tensors:</p>
|
||||
<p>The main comparison in the Technical Report is with SoftRasterizer [<a href="#2">2</a>]. The SoftRasterizer forward CUDA kernel only outputs one <code>(N, H, W, 4)</code> FloatTensor compared with the PyTorch3D rasterizer forward CUDA kernel which outputs 4 tensors:</p>
|
||||
<ul>
|
||||
<li><code>pix_to_face</code>, LongTensor <code>(N, H, W, K)</code></li>
|
||||
<li><code>zbuf</code>, FloatTensor <code>(N, H, W, K)</code></li>
|
||||
@@ -123,8 +100,6 @@ total_memory = memory_forward_pass + memory_backward_pass
|
||||
</code></pre>
|
||||
<p>We need 48 bytes per face per pixel of the rasterized output. In order to remain within bounds for memory usage we can vary the batch size (<strong>N</strong>), image size (<strong>H/W</strong>) and faces per pixel (<strong>K</strong>). For example, for a fixed batch size, if using a larger image size, try reducing the faces per pixel.</p>
|
||||
<hr>
|
||||
<h3><a class="anchor" aria-hidden="true" id="3-modular-design-for-easy-experimentation-and-extensibility"></a><a href="#3-modular-design-for-easy-experimentation-and-extensibility" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>3. Modular design for easy experimentation and extensibility.</h3>
|
||||
<p>We redesigned the rendering pipeline from the ground up to be modular and extensible and challenged many of the limitations in existing libraries. Refer to <a href="renderer_getting_started.md">/docs/renderer_getting_started</a> for a detailed description of the architecture.</p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="references"></a><a href="#references" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>References</h3>
|
||||
<p><a id="1">[1]</a> Kato et al, 'Neural 3D Mesh Renderer', CVPR 2018</p>
|
||||
<p><a id="2">[2]</a> Liu et al, 'Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning', ICCV 2019</p>
|
||||
@@ -134,4 +109,4 @@ total_memory = memory_forward_pass + memory_backward_pass
|
||||
<p><a id="6">[6]</a> Yifan et al, 'Differentiable Surface Splatting for Point-based Geometry Processing', SIGGRAPH Asia 2019</p>
|
||||
<p><a id="7">[7]</a> Loubet et al, 'Reparameterizing Discontinuous Integrands for Differentiable Rendering', SIGGRAPH Asia 2019</p>
|
||||
<p><a id="8">[8]</a> Chen et al, 'Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer', NeurIPS 2019</p>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Patrick Labatut</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/meshes_io"><span class="arrow-prev">← </span><span>Loading from file</span></a><a class="docs-next button" href="/docs/renderer_getting_started"><span>Getting Started</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#uget-startedu"><u>Get started</u></a></li><li><a href="#ukey-featuresu"><u>Key features</u></a><ul class="toc-headings"><li><a href="#1-cuda-support-for-fast-rasterization-of-large-meshes">1. CUDA support for fast rasterization of large meshes</a></li><li><a href="#2-support-for-heterogeneous-batches">2. Support for Heterogeneous Batches</a></li><li><a href="#3-modular-design-for-easy-experimentation-and-extensibility">3. Modular design for easy experimentation and extensibility.</a></li><li><a href="#references">References</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/cubify"><span class="arrow-prev">← </span><span>Cubify</span></a><a class="docs-next button" href="/docs/renderer_getting_started"><span>Getting Started</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#uget-startedu"><u>Get started</u></a></li><li><a href="#utech-reportu"><u>Tech Report</u></a><ul class="toc-headings"><li><a href="#references">References</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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ga('create', 'UA-157376881-1', 'auto');
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ga('send', 'pageview');
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</script><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/pytorch3dfavicon.png" alt="PyTorch3D"/><h2 class="headerTitleWithLogo">PyTorch3D</h2></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/why_pytorch3d" target="_self">Docs</a></li><li class=""><a href="/tutorials" target="_self">Tutorials</a></li><li class=""><a href="https://pytorch3d.readthedocs.io/" target="_self">API</a></li><li class=""><a href="https://github.com/facebookresearch/pytorch3d" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Differentiable Renderer</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Introduction</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/why_pytorch3d">Why PyTorch3D</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Meshes</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/batching">Batching</a></li><li class="navListItem"><a class="navItem" href="/docs/meshes_io">Loading from file</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Differentiable Renderer</h3><ul class=""><li class="navListItem navListItemActive"><a class="navItem" href="/docs/renderer">Overview</a></li><li class="navListItem"><a class="navItem" href="/docs/renderer_getting_started">Getting Started</a></li></ul></div></div></section></div><script>
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</script><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/pytorch3dfavicon.png" alt="PyTorch3D"/><h2 class="headerTitleWithLogo">PyTorch3D</h2></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/why_pytorch3d" target="_self">Docs</a></li><li class=""><a href="/tutorials" target="_self">Tutorials</a></li><li class=""><a href="https://pytorch3d.readthedocs.io/" target="_self">API</a></li><li class=""><a href="https://github.com/facebookresearch/pytorch3d" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Renderer</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Introduction</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/why_pytorch3d">Why PyTorch3D</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Data</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/meshes_io">Loading from file</a></li><li class="navListItem"><a class="navItem" href="/docs/datasets">Data loaders</a></li><li class="navListItem"><a class="navItem" href="/docs/batching">Batching</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Ops</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/cubify">Cubify</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Renderer</h3><ul class=""><li class="navListItem navListItemActive"><a class="navItem" href="/docs/renderer">Overview</a></li><li class="navListItem"><a class="navItem" href="/docs/renderer_getting_started">Getting Started</a></li><li class="navListItem"><a class="navItem" href="/docs/cameras">Cameras</a></li></ul></div></div></section></div><script>
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var coll = document.getElementsByClassName('collapsible');
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var checkActiveCategory = true;
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for (var i = 0; i < coll.length; i++) {
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||||
@@ -72,38 +72,15 @@
|
||||
<li>existing methods either do not support batching or assume that meshes in a batch have the same number of vertices and faces</li>
|
||||
<li>existing projects only provide CUDA implementations so they cannot be used without GPUs</li>
|
||||
</ul>
|
||||
<p>In order to experiment with different approaches, we wanted a modular implementation that is easy to use and extend, and supports <a href="/docs/batching">heterogeneous batching</a>.</p>
|
||||
<p>Taking inspiration from existing work [<a href="#1">1</a>, <a href="#2">2</a>], we have created a new, modular, differentiable renderer with <strong>parallel implementations in PyTorch, C++ and CUDA</strong>, as well as comprehensive documentation and tests, with the aim of helping to further research in this field.</p>
|
||||
<p>In order to experiment with different approaches, we wanted a modular implementation that is easy to use and extend, and supports <a href="/docs/batching">heterogeneous batching</a>. Taking inspiration from existing work [<a href="#1">1</a>, <a href="#2">2</a>], we have created a new, modular, differentiable renderer with <strong>parallel implementations in PyTorch, C++ and CUDA</strong>, as well as comprehensive documentation and tests, with the aim of helping to further research in this field.</p>
|
||||
<p>Our implementation decouples the rasterization and shading steps of rendering. The core rasterization step (based on <a href="#2">[2]</a>) returns several intermediate variables and has an optimized implementation in CUDA. The rest of the pipeline is implemented purely in PyTorch, and is designed to be customized and extended. With this approach, the PyTorch3D differentiable renderer can be imported as a library.</p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="uget-startedu"></a><a href="#uget-startedu" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><u>Get started</u></h2>
|
||||
<p>To learn about more the implementation and start using the renderer refer to <a href="renderer_getting_started.md">/docs/renderer_getting_started</a>, which also contains the <a href="/docs/assets/architecture_overview.png">architecture overview</a> and <a href="/docs/assets/transformations_overview.png">coordinate transformation conventions</a>.</p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="ukey-featuresu"></a><a href="#ukey-featuresu" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><u>Key features</u></h2>
|
||||
<h3><a class="anchor" aria-hidden="true" id="1-cuda-support-for-fast-rasterization-of-large-meshes"></a><a href="#1-cuda-support-for-fast-rasterization-of-large-meshes" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>1. CUDA support for fast rasterization of large meshes</h3>
|
||||
<p>We implemented modular CUDA kernels for the forward and backward pass of rasterization, adaptating a traditional graphics approach known as "coarse-to-fine" rasterization.</p>
|
||||
<p>First, the image is divided into a coarse grid and mesh faces are allocated to the grid cell in which they occur. This is followed by a refinement step which does pixel wise rasterization of the reduced subset of faces per grid cell. The grid cell size is a parameter which can be varied (<code>bin_size</code>).</p>
|
||||
<p>We additionally introduce a parameter <code>faces_per_pixel</code> which allows users to specify the top K faces which should be returned per pixel in the image (as opposed to traditional rasterization which returns only the index of the closest face in the mesh per pixel). The top K face properties can then be aggregated using different methods (such as the sigmoid/softmax approach proposed by Li et at in SoftRasterizer <a href="#2">[2]</a>).</p>
|
||||
<p>We compared PyTorch3D with SoftRasterizer to measure the effect of both these design changes on the speed of rasterization. We selected a set of meshes of different sizes from ShapeNetV1 core, and rasterized one mesh in each batch to produce images of different sizes. We report the speed of the forward and backward passes.</p>
|
||||
<p><strong>Fig 1: PyTorch3D Naive vs Coarse-to-fine</strong></p>
|
||||
<p>This figure shows how the coarse-to-fine strategy for rasterization results in significant speed up compared to naive rasterization for large image size and large mesh sizes.</p>
|
||||
<p><img src="assets/p3d_naive_vs_coarse.png" width="1000"></p>
|
||||
<p>For small mesh and image sizes, the naive approach is slightly faster. We advise that you understand the data you are using and choose the rasterization setting which suits your performance requirements. It is easy to switch between the naive and coarse-to-fine options by adjusting the <code>bin_size</code> value when initializing the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/rasterizer.py#L26">rasterization settings</a>.</p>
|
||||
<p>Setting <code>bin_size = 0</code> will enable naive rasterization. If <code>bin_size > 0</code>, the coarse-to-fine approach is used. The default is <code>bin_size = None</code> in which case we set the bin size based on <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/rasterize_meshes.py#L92">heuristics</a>.</p>
|
||||
<p><strong>Fig 2: PyTorch3D Coarse-to-fine vs SoftRasterizer</strong></p>
|
||||
<p>This figure shows the effect of the <em>combination</em> of coarse-to-fine rasterization and caching the faces rasterized per pixel returned from the forward pass. For large meshes and image sizes, we again observe that the PyTorch3D rasterizer is significantly faster, noting that the speed is dominated by the forward pass and the backward pass is very fast.</p>
|
||||
<p>In the SoftRasterizer implementation, in both the forward and backward pass, there is a loop over every single face in the mesh for every pixel in the image. Therefore, the time for the full forward plus backward pass is ~2x the time for the forward pass. For small mesh and image sizes, the SoftRasterizer approach is slightly faster.</p>
|
||||
<p><img src="assets/p3d_vs_softras.png" width="1000"></p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="2-support-for-heterogeneous-batches"></a><a href="#2-support-for-heterogeneous-batches" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>2. Support for Heterogeneous Batches</h3>
|
||||
<p>PyTorch3D supports efficient rendering of batches of meshes where each mesh has different numbers of vertices and faces. This is done without using padded inputs.</p>
|
||||
<p>We again compare with SoftRasterizer which only supports batches of homogeneous meshes and test two cases: 1) a for loop over meshes in the batch, 2) padded inputs, and compare with the native heterogeneous batching support in PyTorch3D.</p>
|
||||
<p>We group meshes from ShapeNet into bins based on the number of faces in the mesh, and sample to compose a batch. We then render images of fixed size and measure the speed of the forward and backward passes.</p>
|
||||
<p>We tested with a range of increasingly large meshes and bin sizes.</p>
|
||||
<p><strong>Fig 3: PyTorch3D heterogeneous batching compared with SoftRasterizer</strong></p>
|
||||
<p><img src="assets/fullset_batch_size_16.png" width="700"/></p>
|
||||
<p>This shows that for large meshes and large bin width (i.e. more variation in mesh size in the batch) the heterogeneous batching approach in PyTorch3D is faster than either of the workarounds with SoftRasterizer.</p>
|
||||
<p>(settings: batch size = 16, mesh sizes in bins ranging from 500-350k faces, image size = 64, faces per pixel = 100)</p>
|
||||
<p>To learn about more the implementation and start using the renderer refer to <a href="/docs/renderer_getting_started">getting started with renderer</a>, which also contains the <a href="/docs/assets/architecture_overview.png">architecture overview</a> and <a href="/docs/assets/transformations_overview.png">coordinate transformation conventions</a>.</p>
|
||||
<h2><a class="anchor" aria-hidden="true" id="utech-reportu"></a><a href="#utech-reportu" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a><u>Tech Report</u></h2>
|
||||
<p>For an in depth explanation of the renderer design, key features and benchmarks please refer to the PyTorch3D Technical Report on ArXiv: <a href="https://arxiv.org/abs/2007.08501">Accelerating 3D Deep Learning with PyTorch3D</a></p>
|
||||
<hr>
|
||||
<p><strong>NOTE: CUDA Memory usage</strong></p>
|
||||
<p>The SoftRasterizer forward CUDA kernel only outputs one <code>(N, H, W, 4)</code> FloatTensor compared with the PyTorch3D rasterizer forward CUDA kernel which outputs 4 tensors:</p>
|
||||
<p>The main comparison in the Technical Report is with SoftRasterizer [<a href="#2">2</a>]. The SoftRasterizer forward CUDA kernel only outputs one <code>(N, H, W, 4)</code> FloatTensor compared with the PyTorch3D rasterizer forward CUDA kernel which outputs 4 tensors:</p>
|
||||
<ul>
|
||||
<li><code>pix_to_face</code>, LongTensor <code>(N, H, W, K)</code></li>
|
||||
<li><code>zbuf</code>, FloatTensor <code>(N, H, W, K)</code></li>
|
||||
@@ -123,8 +100,6 @@ total_memory = memory_forward_pass + memory_backward_pass
|
||||
</code></pre>
|
||||
<p>We need 48 bytes per face per pixel of the rasterized output. In order to remain within bounds for memory usage we can vary the batch size (<strong>N</strong>), image size (<strong>H/W</strong>) and faces per pixel (<strong>K</strong>). For example, for a fixed batch size, if using a larger image size, try reducing the faces per pixel.</p>
|
||||
<hr>
|
||||
<h3><a class="anchor" aria-hidden="true" id="3-modular-design-for-easy-experimentation-and-extensibility"></a><a href="#3-modular-design-for-easy-experimentation-and-extensibility" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>3. Modular design for easy experimentation and extensibility.</h3>
|
||||
<p>We redesigned the rendering pipeline from the ground up to be modular and extensible and challenged many of the limitations in existing libraries. Refer to <a href="renderer_getting_started.md">/docs/renderer_getting_started</a> for a detailed description of the architecture.</p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="references"></a><a href="#references" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>References</h3>
|
||||
<p><a id="1">[1]</a> Kato et al, 'Neural 3D Mesh Renderer', CVPR 2018</p>
|
||||
<p><a id="2">[2]</a> Liu et al, 'Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning', ICCV 2019</p>
|
||||
@@ -134,4 +109,4 @@ total_memory = memory_forward_pass + memory_backward_pass
|
||||
<p><a id="6">[6]</a> Yifan et al, 'Differentiable Surface Splatting for Point-based Geometry Processing', SIGGRAPH Asia 2019</p>
|
||||
<p><a id="7">[7]</a> Loubet et al, 'Reparameterizing Discontinuous Integrands for Differentiable Rendering', SIGGRAPH Asia 2019</p>
|
||||
<p><a id="8">[8]</a> Chen et al, 'Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer', NeurIPS 2019</p>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Patrick Labatut</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/meshes_io"><span class="arrow-prev">← </span><span>Loading from file</span></a><a class="docs-next button" href="/docs/renderer_getting_started"><span>Getting Started</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#uget-startedu"><u>Get started</u></a></li><li><a href="#ukey-featuresu"><u>Key features</u></a><ul class="toc-headings"><li><a href="#1-cuda-support-for-fast-rasterization-of-large-meshes">1. CUDA support for fast rasterization of large meshes</a></li><li><a href="#2-support-for-heterogeneous-batches">2. Support for Heterogeneous Batches</a></li><li><a href="#3-modular-design-for-easy-experimentation-and-extensibility">3. Modular design for easy experimentation and extensibility.</a></li><li><a href="#references">References</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/cubify"><span class="arrow-prev">← </span><span>Cubify</span></a><a class="docs-next button" href="/docs/renderer_getting_started"><span>Getting Started</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#uget-startedu"><u>Get started</u></a></li><li><a href="#utech-reportu"><u>Tech Report</u></a><ul class="toc-headings"><li><a href="#references">References</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>renderer_getting_started · PyTorch3D</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="# Getting Started With Renderer"/><meta name="docsearch:language" content="en"/><meta property="og:title" content="renderer_getting_started · PyTorch3D"/><meta property="og:type" content="website"/><meta property="og:url" content="https://pytorch3d.org/"/><meta property="og:description" content="# Getting Started With Renderer"/><meta property="og:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><link rel="shortcut icon" href="/img/pytorch3dfavicon.png"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css"/><script>
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ga('send', 'pageview');
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</script><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/pytorch3dfavicon.png" alt="PyTorch3D"/><h2 class="headerTitleWithLogo">PyTorch3D</h2></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/why_pytorch3d" target="_self">Docs</a></li><li class=""><a href="/tutorials" target="_self">Tutorials</a></li><li class=""><a href="https://pytorch3d.readthedocs.io/" target="_self">API</a></li><li class=""><a href="https://github.com/facebookresearch/pytorch3d" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Differentiable Renderer</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Introduction</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/why_pytorch3d">Why PyTorch3D</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Meshes</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/batching">Batching</a></li><li class="navListItem"><a class="navItem" href="/docs/meshes_io">Loading from file</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Differentiable Renderer</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/renderer">Overview</a></li><li class="navListItem navListItemActive"><a class="navItem" href="/docs/renderer_getting_started">Getting Started</a></li></ul></div></div></section></div><script>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="renderer-getting-started"></a><a href="#renderer-getting-started" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Renderer Getting Started</h1>
|
||||
</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="getting-started-with-renderer"></a><a href="#getting-started-with-renderer" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Getting Started With Renderer</h1>
|
||||
<h3><a class="anchor" aria-hidden="true" id="architecture-overview"></a><a href="#architecture-overview" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Architecture Overview</h3>
|
||||
<p>The renderer is designed to be modular, extensible and support batching and gradients for all inputs. The following figure describes all the components of the rendering pipeline.</p>
|
||||
<p><img src="assets/architecture_overview.png" width="1000"></p>
|
||||
<p><img src="assets/architecture_renderer.jpg" width="1000"></p>
|
||||
<h5><a class="anchor" aria-hidden="true" id="fragments"></a><a href="#fragments" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Fragments</h5>
|
||||
<p>The <strong>rasterizer</strong> returns 4 output tensors in a named tuple.</p>
|
||||
<ul>
|
||||
@@ -82,9 +82,9 @@ giving the barycentric coordinates in NDC units of the nearest faces at each pix
|
||||
<hr>
|
||||
<h3><a class="anchor" aria-hidden="true" id="coordinate-transformation-conventions"></a><a href="#coordinate-transformation-conventions" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Coordinate transformation conventions</h3>
|
||||
<p>Rendering requires transformations between several different coordinate frames: world space, view/camera space, NDC space and screen space. At each step it is important to know where the camera is located, how the +X, +Y, +Z axes are aligned and the possible range of values. The following figure outlines the conventions used PyTorch3D.</p>
|
||||
<p><img src="assets/transformations_overview.png" width="1000"></p>
|
||||
<p><img src="assets/transforms_overview.jpg" width="1000"></p>
|
||||
<p>For example, given a teapot mesh, the world coordinate frame, camera coordiante frame and image are show in the figure below. Note that the world and camera coordinate frames have the +z direction pointing in to the page.</p>
|
||||
<p><img src="assets/world_camera_image.png" width="1000"></p>
|
||||
<p><img src="assets/world_camera_image.jpg" width="1000"></p>
|
||||
<hr>
|
||||
<p><strong>NOTE: PyTorch3D vs OpenGL</strong></p>
|
||||
<p>While we tried to emulate several aspects of OpenGL, there are differences in the coordinate frame conventions.</p>
|
||||
@@ -94,18 +94,26 @@ giving the barycentric coordinates in NDC units of the nearest faces at each pix
|
||||
</ul>
|
||||
<p><img align="center" src="assets/opengl_coordframes.png" width="300"></p>
|
||||
<hr>
|
||||
<h3><a class="anchor" aria-hidden="true" id="texturing-options"></a><a href="#texturing-options" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Texturing options</h3>
|
||||
<p>For mesh texturing we offer several options (in <code>pytorch3d/renderer/mesh/texturing.py</code>):</p>
|
||||
<ol>
|
||||
<li><strong>Vertex Textures</strong>: D dimensional textures for each vertex (for example an RGB color) which can be interpolated across the face. This can be represented as an <code>(N, V, D)</code> tensor. This is a fairly simple representation though and cannot model complex textures if the mesh faces are large.</li>
|
||||
<li><strong>UV Textures</strong>: vertex UV coordinates and <strong>one</strong> texture map for the whole face. For a point on a face with given barycentric coordinates, the face color can be computed by interpolating the vertex uv coordinates and then sampling from the texture map. This representation requires two tensors (UVs: <code>(N, V, 2), Texture map:</code>(N, H, W, 3)`), and is limited to only support one texture map per mesh.</li>
|
||||
<li><strong>Face Textures</strong>: In more complex cases such as ShapeNet meshes, there are multiple texture maps per mesh and some faces have texture while other do not. For these cases, a more flexible representation is a texture atlas, where each face is represented as an <code>(RxR)</code> texture map where R is the texture resolution. For a given point on the face, the texture value can be sampled from the per face texture map using the barycentric coordinates of the point. This representation requires one tensor of shape <code>(N, F, R, R, 3)</code>. This texturing method is inspired by the SoftRasterizer implementation. For more details refer to the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/io/mtl_io.py#L123"><code>make_material_atlas</code></a> and <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/textures.py#L452"><code>sample_textures</code></a> functions.</li>
|
||||
</ol>
|
||||
<p><img src="assets/texturing.jpg" width="1000"></p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="a-simple-renderer"></a><a href="#a-simple-renderer" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>A simple renderer</h3>
|
||||
<p>A renderer in PyTorch3D is composed of a <strong>rasterizer</strong> and a <strong>shader</strong>. Create a renderer in a few simple steps:</p>
|
||||
<pre><code class="hljs"><span class="hljs-comment"># Imports</span>
|
||||
<span class="hljs-keyword">from</span> pytorch3d.renderer import (
|
||||
OpenGLPerspectiveCameras, look_at_view_transform,
|
||||
FoVPerspectiveCameras, look_at_view_transform,
|
||||
RasterizationSettings, BlendParams,
|
||||
MeshRenderer, MeshRasterizer, HardPhongShader
|
||||
)
|
||||
|
||||
<span class="hljs-comment"># Initialize an OpenGL perspective camera.</span>
|
||||
R, T = look_at_view_transform(2.7, 10, 20)
|
||||
cameras = OpenGLPerspectiveCameras(<span class="hljs-attribute">device</span>=device, <span class="hljs-attribute">R</span>=R, <span class="hljs-attribute">T</span>=T)
|
||||
cameras = FoVPerspectiveCameras(<span class="hljs-attribute">device</span>=device, <span class="hljs-attribute">R</span>=R, <span class="hljs-attribute">T</span>=T)
|
||||
|
||||
<span class="hljs-comment"># Define the settings for rasterization and shading. Here we set the output image to be of size</span>
|
||||
<span class="hljs-comment"># 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1</span>
|
||||
@@ -114,7 +122,6 @@ raster_settings = RasterizationSettings(
|
||||
<span class="hljs-attribute">image_size</span>=512,
|
||||
<span class="hljs-attribute">blur_radius</span>=0.0,
|
||||
<span class="hljs-attribute">faces_per_pixel</span>=1,
|
||||
<span class="hljs-attribute">bin_size</span>=0
|
||||
)
|
||||
|
||||
<span class="hljs-comment"># Create a phong renderer by composing a rasterizer and a shader. Here we can use a predefined</span>
|
||||
@@ -135,16 +142,15 @@ renderer = MeshRenderer(
|
||||
<p>We have examples of several combinations of these functions based on the texturing/shading/blending support we have currently. These are summarised in this table below. Many other combinations are possible and we plan to expand the options available for texturing, shading and blending.</p>
|
||||
<table>
|
||||
<thead>
|
||||
<tr><th>Example Shaders</th><th style="text-align:center">Vertex Textures</th><th style="text-align:center">Texture Map</th><th style="text-align:center">Flat Shading</th><th style="text-align:center">Gouraud Shading</th><th style="text-align:center">Phong Shading</th><th style="text-align:center">Hard blending</th><th style="text-align:center">Soft Blending</th></tr>
|
||||
<tr><th>Example Shaders</th><th style="text-align:center">Vertex Textures</th><th style="text-align:center">UV Textures</th><th style="text-align:center">Textures Atlas</th><th style="text-align:center">Flat Shading</th><th style="text-align:center">Gouraud Shading</th><th style="text-align:center">Phong Shading</th><th style="text-align:center">Hard blending</th><th style="text-align:center">Soft Blending</th></tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr><td>HardPhongShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftPhongShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>HardGouraudShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftGouraudShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>TexturedSoftPhongShader</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>HardFlatShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftSilhouetteShader</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>HardPhongShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftPhongShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td></tr>
|
||||
<tr><td>HardGouraudShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftGouraudShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td></tr>
|
||||
<tr><td>HardFlatShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftSilhouetteShader</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td></tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Patrick Labatut</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/renderer"><span class="arrow-prev">← </span><span>Overview</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/renderer"><span class="arrow-prev">← </span><span>Overview</span></a><a class="docs-next button" href="/docs/cameras"><span>Cameras</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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}
|
||||
});
|
||||
</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="renderer-getting-started"></a><a href="#renderer-getting-started" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Renderer Getting Started</h1>
|
||||
</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="getting-started-with-renderer"></a><a href="#getting-started-with-renderer" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Getting Started With Renderer</h1>
|
||||
<h3><a class="anchor" aria-hidden="true" id="architecture-overview"></a><a href="#architecture-overview" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Architecture Overview</h3>
|
||||
<p>The renderer is designed to be modular, extensible and support batching and gradients for all inputs. The following figure describes all the components of the rendering pipeline.</p>
|
||||
<p><img src="assets/architecture_overview.png" width="1000"></p>
|
||||
<p><img src="assets/architecture_renderer.jpg" width="1000"></p>
|
||||
<h5><a class="anchor" aria-hidden="true" id="fragments"></a><a href="#fragments" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Fragments</h5>
|
||||
<p>The <strong>rasterizer</strong> returns 4 output tensors in a named tuple.</p>
|
||||
<ul>
|
||||
@@ -82,9 +82,9 @@ giving the barycentric coordinates in NDC units of the nearest faces at each pix
|
||||
<hr>
|
||||
<h3><a class="anchor" aria-hidden="true" id="coordinate-transformation-conventions"></a><a href="#coordinate-transformation-conventions" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Coordinate transformation conventions</h3>
|
||||
<p>Rendering requires transformations between several different coordinate frames: world space, view/camera space, NDC space and screen space. At each step it is important to know where the camera is located, how the +X, +Y, +Z axes are aligned and the possible range of values. The following figure outlines the conventions used PyTorch3D.</p>
|
||||
<p><img src="assets/transformations_overview.png" width="1000"></p>
|
||||
<p><img src="assets/transforms_overview.jpg" width="1000"></p>
|
||||
<p>For example, given a teapot mesh, the world coordinate frame, camera coordiante frame and image are show in the figure below. Note that the world and camera coordinate frames have the +z direction pointing in to the page.</p>
|
||||
<p><img src="assets/world_camera_image.png" width="1000"></p>
|
||||
<p><img src="assets/world_camera_image.jpg" width="1000"></p>
|
||||
<hr>
|
||||
<p><strong>NOTE: PyTorch3D vs OpenGL</strong></p>
|
||||
<p>While we tried to emulate several aspects of OpenGL, there are differences in the coordinate frame conventions.</p>
|
||||
@@ -94,18 +94,26 @@ giving the barycentric coordinates in NDC units of the nearest faces at each pix
|
||||
</ul>
|
||||
<p><img align="center" src="assets/opengl_coordframes.png" width="300"></p>
|
||||
<hr>
|
||||
<h3><a class="anchor" aria-hidden="true" id="texturing-options"></a><a href="#texturing-options" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Texturing options</h3>
|
||||
<p>For mesh texturing we offer several options (in <code>pytorch3d/renderer/mesh/texturing.py</code>):</p>
|
||||
<ol>
|
||||
<li><strong>Vertex Textures</strong>: D dimensional textures for each vertex (for example an RGB color) which can be interpolated across the face. This can be represented as an <code>(N, V, D)</code> tensor. This is a fairly simple representation though and cannot model complex textures if the mesh faces are large.</li>
|
||||
<li><strong>UV Textures</strong>: vertex UV coordinates and <strong>one</strong> texture map for the whole face. For a point on a face with given barycentric coordinates, the face color can be computed by interpolating the vertex uv coordinates and then sampling from the texture map. This representation requires two tensors (UVs: <code>(N, V, 2), Texture map:</code>(N, H, W, 3)`), and is limited to only support one texture map per mesh.</li>
|
||||
<li><strong>Face Textures</strong>: In more complex cases such as ShapeNet meshes, there are multiple texture maps per mesh and some faces have texture while other do not. For these cases, a more flexible representation is a texture atlas, where each face is represented as an <code>(RxR)</code> texture map where R is the texture resolution. For a given point on the face, the texture value can be sampled from the per face texture map using the barycentric coordinates of the point. This representation requires one tensor of shape <code>(N, F, R, R, 3)</code>. This texturing method is inspired by the SoftRasterizer implementation. For more details refer to the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/io/mtl_io.py#L123"><code>make_material_atlas</code></a> and <a href="https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/renderer/mesh/textures.py#L452"><code>sample_textures</code></a> functions.</li>
|
||||
</ol>
|
||||
<p><img src="assets/texturing.jpg" width="1000"></p>
|
||||
<h3><a class="anchor" aria-hidden="true" id="a-simple-renderer"></a><a href="#a-simple-renderer" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>A simple renderer</h3>
|
||||
<p>A renderer in PyTorch3D is composed of a <strong>rasterizer</strong> and a <strong>shader</strong>. Create a renderer in a few simple steps:</p>
|
||||
<pre><code class="hljs"><span class="hljs-comment"># Imports</span>
|
||||
<span class="hljs-keyword">from</span> pytorch3d.renderer import (
|
||||
OpenGLPerspectiveCameras, look_at_view_transform,
|
||||
FoVPerspectiveCameras, look_at_view_transform,
|
||||
RasterizationSettings, BlendParams,
|
||||
MeshRenderer, MeshRasterizer, HardPhongShader
|
||||
)
|
||||
|
||||
<span class="hljs-comment"># Initialize an OpenGL perspective camera.</span>
|
||||
R, T = look_at_view_transform(2.7, 10, 20)
|
||||
cameras = OpenGLPerspectiveCameras(<span class="hljs-attribute">device</span>=device, <span class="hljs-attribute">R</span>=R, <span class="hljs-attribute">T</span>=T)
|
||||
cameras = FoVPerspectiveCameras(<span class="hljs-attribute">device</span>=device, <span class="hljs-attribute">R</span>=R, <span class="hljs-attribute">T</span>=T)
|
||||
|
||||
<span class="hljs-comment"># Define the settings for rasterization and shading. Here we set the output image to be of size</span>
|
||||
<span class="hljs-comment"># 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1</span>
|
||||
@@ -114,7 +122,6 @@ raster_settings = RasterizationSettings(
|
||||
<span class="hljs-attribute">image_size</span>=512,
|
||||
<span class="hljs-attribute">blur_radius</span>=0.0,
|
||||
<span class="hljs-attribute">faces_per_pixel</span>=1,
|
||||
<span class="hljs-attribute">bin_size</span>=0
|
||||
)
|
||||
|
||||
<span class="hljs-comment"># Create a phong renderer by composing a rasterizer and a shader. Here we can use a predefined</span>
|
||||
@@ -135,16 +142,15 @@ renderer = MeshRenderer(
|
||||
<p>We have examples of several combinations of these functions based on the texturing/shading/blending support we have currently. These are summarised in this table below. Many other combinations are possible and we plan to expand the options available for texturing, shading and blending.</p>
|
||||
<table>
|
||||
<thead>
|
||||
<tr><th>Example Shaders</th><th style="text-align:center">Vertex Textures</th><th style="text-align:center">Texture Map</th><th style="text-align:center">Flat Shading</th><th style="text-align:center">Gouraud Shading</th><th style="text-align:center">Phong Shading</th><th style="text-align:center">Hard blending</th><th style="text-align:center">Soft Blending</th></tr>
|
||||
<tr><th>Example Shaders</th><th style="text-align:center">Vertex Textures</th><th style="text-align:center">UV Textures</th><th style="text-align:center">Textures Atlas</th><th style="text-align:center">Flat Shading</th><th style="text-align:center">Gouraud Shading</th><th style="text-align:center">Phong Shading</th><th style="text-align:center">Hard blending</th><th style="text-align:center">Soft Blending</th></tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr><td>HardPhongShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftPhongShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>HardGouraudShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftGouraudShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>TexturedSoftPhongShader</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>HardFlatShader</td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftSilhouetteShader</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">:heavy_check_mark:</td></tr>
|
||||
<tr><td>HardPhongShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftPhongShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td></tr>
|
||||
<tr><td>HardGouraudShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftGouraudShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td></tr>
|
||||
<tr><td>HardFlatShader</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center">✔️</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td><td style="text-align:center"></td></tr>
|
||||
<tr><td>SoftSilhouetteShader</td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center"></td><td style="text-align:center">✔️</td></tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Patrick Labatut</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/renderer"><span class="arrow-prev">← </span><span>Overview</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/renderer"><span class="arrow-prev">← </span><span>Overview</span></a><a class="docs-next button" href="/docs/cameras"><span>Cameras</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc<br/>Legal:<a href="https://opensource.facebook.com/legal/privacy/" target="_blank" rel="noreferrer noopener">Privacy</a><a href="https://opensource.facebook.com/legal/terms/" target="_blank" rel="noreferrer noopener">Terms</a></section></footer></div></body></html>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="why-pytorch3d"></a><a href="#why-pytorch3d" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Why PyTorch3D</h1>
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||||
<p>Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as <a href="https://github.com/facebookresearch/meshrcnn">Mesh R-CNN</a> and <a href="https://github.com/facebookresearch/c3dpo_nrsfm">C3DPO</a>, we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area.</p>
|
||||
<p>In PyTorch3D we have included efficient 3D operators, heterogeneous batching capabilities, and a modular differentiable rendering API, to equip researchers in this field with a much needed toolkit to implement cutting-edge research with complex 3D inputs.</p>
|
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Patrick Labatut</em></div><div class="docs-prevnext"><a class="docs-next button" href="/docs/batching"><span>Batching</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="why-pytorch3d"></a><a href="#why-pytorch3d" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Why PyTorch3D</h1>
|
||||
<p>Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as <a href="https://github.com/facebookresearch/meshrcnn">Mesh R-CNN</a> and <a href="https://github.com/facebookresearch/c3dpo_nrsfm">C3DPO</a>, we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area.</p>
|
||||
<p>In PyTorch3D we have included efficient 3D operators, heterogeneous batching capabilities, and a modular differentiable rendering API, to equip researchers in this field with a much needed toolkit to implement cutting-edge research with complex 3D inputs.</p>
|
||||
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Patrick Labatut</em></div><div class="docs-prevnext"><a class="docs-next button" href="/docs/batching"><span>Batching</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 2020 Facebook Inc</section></footer></div></body></html>
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