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<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="/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>
<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_renderer.jpg">architecture overview</a> and <a href="/docs/assets/transforms_overview.jpg">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>, 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>
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@@ -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 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 © 2022 Meta Platforms, 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 Krzysztof Chalupka</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 © 2022 Meta Platforms, 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>