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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>datasets · PyTorch3D</title><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta name="generator" content="Docusaurus"/><meta name="description" content="# Data loaders for common 3D Datasets"/><meta name="docsearch:language" content="en"/><meta property="og:title" content="datasets · PyTorch3D"/><meta property="og:type" content="website"/><meta property="og:url" content="https://pytorch3d.org/"/><meta property="og:description" content="# Data loaders for common 3D Datasets"/><meta property="og:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://pytorch3d.org/img/pytorch3dlogoicon.svg"/><link rel="shortcut icon" href="/img/pytorch3dfavicon.png"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css"/><script>
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</script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"></header><article><div><span><h1><a class="anchor" aria-hidden="true" id="data-loaders-for-common-3d-datasets"></a><a href="#data-loaders-for-common-3d-datasets" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data loaders for common 3D Datasets</h1>
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<h3><a class="anchor" aria-hidden="true" id="shapetnetcore"></a><a href="#shapetnetcore" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>ShapetNetCore</h3>
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<p>ShapeNet is a dataset of 3D CAD models. ShapeNetCore is a subset of the ShapeNet dataset and can be downloaded from <a href="https://www.shapenet.org/">https://www.shapenet.org/</a>. There are two versions ShapeNetCore: v1 (55 categories) and v2 (57 categories).</p>
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<p>The PyTorch3D <a href="https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/datasets/shapenet/shapenet_core.py">ShapeNetCore data loader</a> inherits from <code>torch.utils.data.Dataset</code>. It takes the path where the ShapeNetCore dataset is stored locally and loads models in the dataset. The ShapeNetCore class loads and returns models with their <code>categories</code>, <code>model_ids</code>, <code>vertices</code> and <code>faces</code>. The <code>ShapeNetCore</code> data loader also has a customized <code>render</code> function that renders models by the specified <code>model_ids (List[int])</code>, <code>categories (List[str])</code> or <code>indices (List[int])</code> with PyTorch3D's differentiable renderer.</p>
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<p>The loaded dataset can be passed to <code>torch.utils.data.DataLoader</code> with PyTorch3D's customized collate_fn: <code>collate_batched_meshes</code> from the <code>pytorch3d.dataset.utils</code> module. The <code>vertices</code> and <code>faces</code> of the models are used to construct a <a href="https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/structures/meshes.py">Meshes</a> object representing the batched meshes. This <code>Meshes</code> representation can be easily used with other ops and rendering in PyTorch3D.</p>
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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>
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<p>The R2N2 dataset contains 13 categories that are a subset of the ShapeNetCore v.1 dataset. The R2N2 dataset also contains its own 24 renderings of each object and voxelized models. The R2N2 Dataset can be downloaded following the instructions <a href="http://3d-r2n2.stanford.edu/">here</a>.</p>
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<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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