pipablepytorch3d
PyTorch3D is FAIR's library of reusable components for deep Learning with 3D data.
Decision gist · record as of 2026-08-14
Yes. PyTorch3D is production-stable, actively maintained, has low install friction, carries a permissive BSD license, and addresses a clear need in 3D deep learning research. The only caveat is that it does not yet support Python 3.12, so verify your environment compatibility first.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 through 3.11; Python 3.12 is not supported.
- Low friction installation with only two runtime dependencies (iopath and fvcore).
- Actively maintained with recent commits and stable production status, though capped at Python 3.11 and does not yet support Python 3.12.
License · maintenance · safety
permissive license (permissive) — BSD License permits commercial and private use with minimal restrictions; you must retain copyright notices and disclaimers in source and binary distributions, but can modify and redistribute freely.
last release 2024-07-09 (766 days) · last repo commit 2026-08-13 · 9,949 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,185,293 downloads/mo, #4,252 on PyPI
Alternatives
Verify before relying
pip install pipablepytorch3d
from iopath import HTTPPathHandler
from fvcore.common.config import CfgNode
# Use iopath for file I/O and fvcore for configuration- Whether GPU or CUDA toolkit is required versus optional for functionality.
- Performance characteristics and memory requirements for typical mesh operations.
- Specific PyTorch version compatibility beyond the stated Python version range.
What it is and what it does
PyTorch3D is a library for 3D deep learning research that provides data structures and operations for working with 3D geometry—primarily triangle meshes and point clouds. All operations are implemented using tensors, handle minibatches of heterogeneous data, are differentiable, and can utilize GPUs for acceleration. The library includes a differentiable mesh renderer, utilities for mesh manipulation, and Implicitron, a framework for implicit 3D representations.
The package depends on iopath for file I/O and fvcore for core utilities. It is actively maintained with production-stable status and supports Python 3.8, 3.9, 3.10, and 3.11. The library is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
Use it for
- Train neural networks to predict 3D mesh geometry using differentiable rendering as a loss signal.
- Implement mesh deformation and optimization tasks like fitting template meshes to target geometry.
- Render 3D scenes with textured meshes or point clouds for visualization or learning pipelines.
- Build implicit neural representation models for novel-view synthesis or 3D reconstruction.
- Process and batch heterogeneous 3D data in a single forward pass.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PyTorch3D is production-stable, actively maintained, has low install friction, carries a permissive BSD license, and addresses a clear need in 3D deep learning research. The only caveat is that it does not yet support Python 3.12, so verify your environment compatibility first.
Install
pipablepytorch3d on PyPI
Before you install
Low friction installation with only two runtime dependencies (iopath and fvcore). Actively maintained with recent commits and stable production status, though capped at Python 3.11 and does not yet support Python 3.12.
Requires Python 3.8 through 3.11; Python 3.12 is not supported.
License in practice
BSD License permits commercial and private use with minimal restrictions; you must retain copyright notices and disclaimers in source and binary distributions, but can modify and redistribute freely.
Quickstart
pip install pipablepytorch3d
from iopath import HTTPPathHandler
from fvcore.common.config import CfgNode
# Use iopath for file I/O and fvcore for configurationVerify before relying
- Whether GPU or CUDA toolkit is required versus optional for functionality.
- Performance characteristics and memory requirements for typical mesh operations.
- Specific PyTorch version compatibility beyond the stated Python version range.
Package facts
| License | permissive license permissive |
| Python support | Capped below the current Python release <3.12,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesiopathfvcore |
| Maintenance | Actively maintained 766 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,185,293 / month, #4,252 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: pipablepytorch3d-0.7.6-py3-none-any.whl
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See also tensorflow-graphics · manifold3d · pymeshlab · open3d · PyGEL3D · open3d-cpu · triangle · cytriangle · fvcore · pytorchcv