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<h3><a class="anchor" aria-hidden="true" id="r2n2"></a><a href="#r2n2" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>R2N2</h3>
<p>The R2N2 dataset contains 13 categories that are a subset of the ShapeNetCore v.1 dataset. The R2N2 dataset also contains its own 24 renderings of each object and voxelized models. The R2N2 Dataset can be downloaded following the instructions <a href="http://3d-r2n2.stanford.edu/">here</a>.</p>
<p>The PyTorch3D <a href="https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/datasets/r2n2/r2n2.py">R2N2 data loader</a> is initialized with the paths to the ShapeNet dataset, the R2N2 dataset and the splits file for R2N2. Just like <code>ShapeNetCore</code>, it can be passed to <code>torch.utils.data.DataLoader</code> with a customized collate_fn: <code>collate_batched_R2N2</code> from the <code>pytorch3d.dataset.r2n2.utils</code> module. It returns all the data that <code>ShapeNetCore</code> returns, and in addition, it returns the R2N2 renderings (24 views for each model) along with the camera calibration matrices and a voxel representation for each model. Similar to <code>ShapeNetCore</code>, it has a customized <code>render</code> function that supports rendering specified models with the PyTorch3D differentiable renderer. In addition, it supports rendering models with the same orientations as R2N2's original renderings.</p>
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</span></div></article></div><div class="docLastUpdate"><em>Last updated by Jeremy Reizenstein</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/meshes_io"><span class="arrow-prev">← </span><span>Loading from file</span></a><a class="docs-next button" href="/docs/batching"><span>Batching</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><div class="footerSection"><div class="social"><a class="github-button" href="https://github.com/facebookresearch/pytorch3d" data-count-href="https://github.com/facebookresearch/pytorch3d/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star PyTorch3D on GitHub">pytorch3d</a></div></div></section><a href="https://opensource.facebook.com/" target="_blank" rel="noreferrer noopener" class="fbOpenSource"><img src="/img/oss_logo.png" alt="Facebook Open Source" width="170" height="45"/></a><section class="copyright">Copyright © 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>