#8686th of 120 in ML frameworks and librariesFor image, video and design
pytorch3d
PyTorch library for differentiable 3D mesh operations and rendering
- Stars
- 10k
- License
- custom license
- Last commit
- Oct 2026
- Last release
- Nov 2025
- Language
- Python
9,785 on 2 Apr 20269,988 on 22 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
PyTorch3D provides triangle mesh data structures, mesh operations (projective transforms, graph convolution, sampling, losses) and a differentiable mesh renderer, all built on PyTorch tensors with heterogeneous minibatch support. It also includes Implicitron, a framework for novel-view synthesis with implicit representations. It targets 3D computer vision research, with tutorial notebooks for mesh fitting, NeRF and bundle adjustment.
Who it is for: 3D computer vision researchers working in PyTorch
Strengths
- All operators are differentiable, batch heterogeneous data and can run on GPU
- Differentiable mesh renderer plus point cloud and volume rendering
- Tutorial notebooks cover NeRF, bundle adjustment, textured mesh fitting and ShapeNet loading
- Includes Implicitron framework for implicit-representation view synthesis
Weaknesses
- No backward-compatibility guarantee between releases
- main branch and nightly builds may break at any time
- README news list stops at v0.7.5 (Oct 2023)
- License detected as NOASSERTION; README says BSD
What it needs
- GPU optional
- Needs PyTorch
- Models: triangle meshes, point clouds, volumes, implicit representations (NeRF)
Quick answers
Is pytorch3d open source?
Its code is public on GitHub under its own license terms; read them before you use it at work.
Does pytorch3d need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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