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Nikhila Ravi
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<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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<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>
<p><img src="https://user-images.githubusercontent.com/4369065/81032959-af697380-8e46-11ea-91a8-fae89597f988.png" alt="input"></p>
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<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="/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>
<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>, for the pulsar backend see here: <a href="https://arxiv.org/abs/2004.07484">Fast Differentiable Raycasting for Neural Rendering using Sphere-based Representations</a>.</p>
<hr>
<p><strong>NOTE: CUDA Memory usage</strong></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>
@@ -109,4 +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 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>
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Christoph Lassner</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/visualization"><span class="arrow-prev">← </span><span>Plotly Visualization</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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@@ -6,7 +6,7 @@
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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>
</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">Visualization</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/visualization">Plotly Visualization</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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@@ -77,7 +77,7 @@
<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="/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>
<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>, for the pulsar backend see here: <a href="https://arxiv.org/abs/2004.07484">Fast Differentiable Raycasting for Neural Rendering using Sphere-based Representations</a>.</p>
<hr>
<p><strong>NOTE: CUDA Memory usage</strong></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>
@@ -109,4 +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 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>
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Christoph Lassner</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/visualization"><span class="arrow-prev">← </span><span>Plotly Visualization</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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@@ -83,7 +83,7 @@ giving the barycentric coordinates in NDC units of the nearest faces at each pix
<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/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>For example, given a teapot mesh, the world coordinate frame, camera coordinate frame and image are shown 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.jpg" width="1000"></p>
<hr>
<p><strong>NOTE: PyTorch3D vs OpenGL</strong></p>
@@ -94,11 +94,16 @@ 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="the-pulsar-backend"></a><a href="#the-pulsar-backend" 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>The pulsar backend</h3>
<p>Since v0.3, <a href="https://arxiv.org/abs/2004.07484">pulsar</a> can be used as a backend for point-rendering. It has a focus on efficiency, which comes with pros and cons: it is highly optimized and all rendering stages are integrated in the CUDA kernels. This leads to significantly higher speed and better scaling behavior. We use it at Facebook Reality Labs to render and optimize scenes with millions of spheres in resolutions up to 4K. You can find a runtime comparison plot below (settings: <code>bin_size=None</code>, <code>points_per_pixel=5</code>, <code>image_size=1024</code>, <code>radius=1e-2</code>, <code>composite_params.radius=1e-4</code>; benchmarked on an RTX 2070 GPU).</p>
<p><img align="center" src="assets/pulsar_bm.png" width="300"></p>
<p>Pulsar's processing steps are tightly integrated CUDA kernels and do not work with custom <code>rasterizer</code> and <code>compositor</code> components. We provide two ways to use Pulsar: (1) there is a unified interface to match the PyTorch3D calling convention seamlessly. This is, for example, illustrated in the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/render_colored_points.ipynb">point cloud tutorial</a>. (2) There is a direct interface available to the pulsar backend, which exposes the full functionality of the backend (including opacity, which is not yet available in PyTorch3D). Examples showing its use as well as the matching PyTorch3D interface code are available in <a href="https://github.com/facebookresearch/pytorch3d/tree/master/docs/examples">this folder</a>.</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>UV Textures</strong>: vertex UV coordinates and <strong>one</strong> texture map for the whole mesh. 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>
@@ -153,4 +158,4 @@ renderer = MeshRenderer(
<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 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>
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Christoph Lassner</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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@@ -6,7 +6,7 @@
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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"><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><li class="navListItem"><a class="navItem" href="/docs/cameras">Cameras</a></li></ul></div></div></section></div><script>
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@@ -83,7 +83,7 @@ giving the barycentric coordinates in NDC units of the nearest faces at each pix
<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/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>For example, given a teapot mesh, the world coordinate frame, camera coordinate frame and image are shown 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.jpg" width="1000"></p>
<hr>
<p><strong>NOTE: PyTorch3D vs OpenGL</strong></p>
@@ -94,11 +94,16 @@ 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="the-pulsar-backend"></a><a href="#the-pulsar-backend" 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>The pulsar backend</h3>
<p>Since v0.3, <a href="https://arxiv.org/abs/2004.07484">pulsar</a> can be used as a backend for point-rendering. It has a focus on efficiency, which comes with pros and cons: it is highly optimized and all rendering stages are integrated in the CUDA kernels. This leads to significantly higher speed and better scaling behavior. We use it at Facebook Reality Labs to render and optimize scenes with millions of spheres in resolutions up to 4K. You can find a runtime comparison plot below (settings: <code>bin_size=None</code>, <code>points_per_pixel=5</code>, <code>image_size=1024</code>, <code>radius=1e-2</code>, <code>composite_params.radius=1e-4</code>; benchmarked on an RTX 2070 GPU).</p>
<p><img align="center" src="assets/pulsar_bm.png" width="300"></p>
<p>Pulsar's processing steps are tightly integrated CUDA kernels and do not work with custom <code>rasterizer</code> and <code>compositor</code> components. We provide two ways to use Pulsar: (1) there is a unified interface to match the PyTorch3D calling convention seamlessly. This is, for example, illustrated in the <a href="https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/render_colored_points.ipynb">point cloud tutorial</a>. (2) There is a direct interface available to the pulsar backend, which exposes the full functionality of the backend (including opacity, which is not yet available in PyTorch3D). Examples showing its use as well as the matching PyTorch3D interface code are available in <a href="https://github.com/facebookresearch/pytorch3d/tree/master/docs/examples">this folder</a>.</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>UV Textures</strong>: vertex UV coordinates and <strong>one</strong> texture map for the whole mesh. 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>
@@ -153,4 +158,4 @@ renderer = MeshRenderer(
<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 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>
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Christoph Lassner</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="overview"></a><a href="#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>Overview</h1>
<p>PyTorch3D provides a modular differentiable renderer, but for instances where we want interactive plots or are not concerned with the differentiability of the rendering process, we provide <a href="../../pytorch3d/vis/plotly_vis.py">functions to render meshes and pointclouds in plotly</a>. These plotly figures allow you to rotate and zoom the rendered images and support plotting batched data as multiple traces in a singular plot or divided into individual subplots.</p>
<h1><a class="anchor" aria-hidden="true" id="examples"></a><a href="#examples" 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>Examples</h1>
<p>These rendering functions accept plotly x,y, and z axis arguments as <code>kwargs</code>, allowing us to customize the plots. Here are two plots with colored axes, a <a href="/docs/assets/plotly_pointclouds.png">Pointclouds plot</a>, a <a href="/docs/assets/plotly_meshes_batch.png">batched Meshes plot in subplots</a>, and a <a href="/docs/assets/plotly_meshes_trace.png">batched Meshes plot with multiple traces</a>. Refer to the <a href="../tutorials/render_textured_meshes.ipynb">render textured meshes</a> and <a href="../tutorials/render_colored_points">render colored pointclouds</a> tutorials for code examples.</p>
<h1><a class="anchor" aria-hidden="true" id="saving-plots-to-images"></a><a href="#saving-plots-to-images" 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>Saving plots to images</h1>
<p>If you want to save these plotly plots, you will need to install a separate library such as <a href="https://plotly.com/python/static-image-export/">Kaleido</a>.</p>
<p>Install Kaleido</p>
<pre><code class="hljs">$ pip <span class="hljs-keyword">install</span> Kaleido
</code></pre>
<p>Export a figure as a .png image. The image will be saved in the current working directory.</p>
<pre><code class="hljs"><span class="hljs-attribute">fig</span> = ...
fig.write_image(<span class="hljs-string">"image_name.png"</span>)
</code></pre>
</span></div></article></div><div class="docLastUpdate"><em>Last updated by Amitav Baruah</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"><span>Overview</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="overview"></a><a href="#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>Overview</h1>
<p>PyTorch3D provides a modular differentiable renderer, but for instances where we want interactive plots or are not concerned with the differentiability of the rendering process, we provide <a href="../../pytorch3d/vis/plotly_vis.py">functions to render meshes and pointclouds in plotly</a>. These plotly figures allow you to rotate and zoom the rendered images and support plotting batched data as multiple traces in a singular plot or divided into individual subplots.</p>
<h1><a class="anchor" aria-hidden="true" id="examples"></a><a href="#examples" 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>Examples</h1>
<p>These rendering functions accept plotly x,y, and z axis arguments as <code>kwargs</code>, allowing us to customize the plots. Here are two plots with colored axes, a <a href="/docs/assets/plotly_pointclouds.png">Pointclouds plot</a>, a <a href="/docs/assets/plotly_meshes_batch.png">batched Meshes plot in subplots</a>, and a <a href="/docs/assets/plotly_meshes_trace.png">batched Meshes plot with multiple traces</a>. Refer to the <a href="../tutorials/render_textured_meshes.ipynb">render textured meshes</a> and <a href="../tutorials/render_colored_points">render colored pointclouds</a> tutorials for code examples.</p>
<h1><a class="anchor" aria-hidden="true" id="saving-plots-to-images"></a><a href="#saving-plots-to-images" 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>Saving plots to images</h1>
<p>If you want to save these plotly plots, you will need to install a separate library such as <a href="https://plotly.com/python/static-image-export/">Kaleido</a>.</p>
<p>Install Kaleido</p>
<pre><code class="hljs">$ pip <span class="hljs-keyword">install</span> Kaleido
</code></pre>
<p>Export a figure as a .png image. The image will be saved in the current working directory.</p>
<pre><code class="hljs"><span class="hljs-attribute">fig</span> = ...
fig.write_image(<span class="hljs-string">"image_name.png"</span>)
</code></pre>
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