facebookresearch/pytorch3d

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

9954 stars · 1463 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PyTorch3D is Facebook AI Research's library of efficient, reusable components for deep learning with 3D data, built on PyTorch. It provides data structures and operators for triangle meshes and point clouds, heterogeneous batching, and a modular differentiable mesh renderer with CUDA acceleration.

Use cases

• differentiably render 3d meshes in pytorch
• compute chamfer distance between point clouds
• batch 3d meshes of different sizes for deep learning
• train neural networks on triangle mesh data
• new view synthesis with implicit representations
• load and manipulate obj mesh files
• deform 3d meshes with gradient-based optimization

When to choose

• you need differentiable 3D rendering integrated with PyTorch training loops
• you're doing 3D computer vision research with meshes or point clouds on GPU
• you need efficient 3D operators like graph convolution, sampling, or loss functions on heterogeneous batches

When to avoid

• you need a general-purpose 3D modeling or game engine tool
• you work only with 2D images or non-PyTorch frameworks like TensorFlow or JAX
• you need a stable high-level API for production rather than research-oriented components

Facets

library · maturity active

machine-learning deep-learning graphics image-processing math deep-learning computer-vision graphics machine-learning python cross-platform 3d pytorch differentiable-rendering triangle-meshes point-clouds new-view-synthesis computer-vision-research cuda research gpu linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/pytorch3d")