PyTorch3D — 3D Deep Learning Library

PyTorch3D — 3D Deep Learning Library

PyTorch3D is Meta FAIR's (FAIR = Fundamental AI Research) library of reusable components for deep learning with 3D data — meshes, point clouds, and volumes. It's designed to make differentiable rendering and 3D geometry operations as straightforward as conv2d, enabling robot perception, shape reconstruction, and neural radiance field (NeRF) research.

Key features

• Differentiable mesh and point cloud renderers (rasterizer + shader)
• Efficient batched 3D ops: chamfer distance, kNN, graph convolutions
• NeRF / implicit surface utilities (ray-marching, volume rendering)
• IO for common formats: OBJ, PLY, glTF, OFF
• GPU-accelerated CUDA kernels for all core ops

Quick start

conda install pytorch3d -c pytorch3d

import torch
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (FoVPerspectiveCameras, RasterizationSettings,
                                 MeshRenderer, MeshRasterizer, SoftPhongShader)

verts, faces = load_obj_verts("model.obj")
mesh = Meshes(verts=[verts], faces=[faces])
cameras = FoVPerspectiveCameras(device="cuda")
renderer = MeshRenderer(rasterizer=MeshRasterizer(cameras=cameras,
                         raster_settings=RasterizationSettings(image_size=256)),
                        shader=SoftPhongShader(cameras=cameras, device="cuda"))
image = renderer(mesh)

npx ai-supply add pytorch3d-3d-deep-learning

Curated mirror of the open-source PyTorch3D (BSD-3-Clause). Get it from the source.