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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/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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<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/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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@@ -115,4 +115,4 @@ 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>
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</code></pre>
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@@ -115,4 +115,4 @@ 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>
|
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</code></pre>
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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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@@ -128,4 +128,4 @@ total_memory = memory_forward_pass + memory_backward_pass
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<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>
|
||||
</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/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/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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@@ -128,4 +128,4 @@ total_memory = memory_forward_pass + memory_backward_pass
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<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>
|
||||
</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/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/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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<!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="# Renderer Getting Started"/><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="# Renderer Getting Started"/><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><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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<span class="hljs-attribute">shader</span>=PhongShader(device=device, <span class="hljs-attribute">cameras</span>=cameras)
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)
|
||||
</code></pre>
|
||||
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@@ -1,4 +1,4 @@
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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="# Renderer Getting Started"/><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="# Renderer Getting Started"/><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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@@ -119,4 +119,4 @@ renderer = MeshRenderer(
|
||||
<span class="hljs-attribute">shader</span>=PhongShader(device=device, <span class="hljs-attribute">cameras</span>=cameras)
|
||||
)
|
||||
</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"><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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@@ -1,4 +1,4 @@
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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>
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||||
<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 Nikhila Ravi</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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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Nikhila Ravi</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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<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 Nikhila Ravi</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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