pytorch3d/docs/notes/datasets.md

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sidebar_label: Data loaders

Data loaders for common 3D Datasets

ShapetNetCore

ShapeNet is a dataset of 3D CAD models. ShapeNetCore is a subset of the ShapeNet dataset and can be downloaded from https://www.shapenet.org/. There are two versions ShapeNetCore: v1 (55 categories) and v2 (57 categories).

The PyTorch3D ShapeNetCore data loader inherits from torch.utils.data.Dataset. 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 categories, model_ids, vertices and faces. The ShapeNetCore data loader also has a customized render function that renders models by the specified model_ids (List[int]), categories (List[str]) or indices (List[int]) with PyTorch3D's differentiable renderer.

The loaded dataset can be passed to torch.utils.data.DataLoader with PyTorch3D's customized collate_fn: collate_batched_meshes from the pytorch3d.dataset.utils module. The vertices and faces of the models are used to construct a Meshes object representing the batched meshes. This Meshes representation can be easily used with other ops and rendering in PyTorch3D.

R2N2

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 here.

The PyTorch3D R2N2 data loader is initialized with the paths to the ShapeNet dataset, the R2N2 dataset and the splits file for R2N2. Just like ShapeNetCore, it can be passed to torch.utils.data.DataLoader with a customized collate_fn: collate_batched_R2N2 from the pytorch3d.dataset.r2n2.utils module. It returns all the data that ShapeNetCore 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 ShapeNetCore, it has a customized render 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.