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PyTorch3D · A library for deep learning with 3D data 
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A library for deep learning with 3D data
Heterogeneous Batching
Heterogeneous Batching
Supports batching of 3D inputs of different sizes such as meshes
Fast 3D Operators
Fast 3D Operators
Supports optimized implementations of several common functions for 3D data
Differentiable Rendering
Differentiable Rendering
Modular differentiable rendering API with parallel implementations in PyTorch, C++ and CUDA
Get Started
- Install PyTorch3D (following the instructions here)
- Try a few 3D operators e.g. compute the chamfer loss between two meshes:
from pytorch3d.utils import ico_sphere from pytorch3d.io import load_obj from pytorch3d.structures import Meshes from pytorch3d.ops import sample_points_from_meshes from pytorch3d.loss import chamfer_distance # Use an ico_sphere mesh and load a mesh from an .obj e.g. model.obj sphere_mesh = ico_sphere(level=3) verts, faces, _ = load_obj("model.obj") test_mesh = Meshes(verts=[verts], faces=[faces.verts_idx]) # Differentiably sample 5k points from the surface of each mesh and then compute the loss. sample_sphere = sample_points_from_meshes(sphere_mesh, 5000) sample_test = sample_points_from_meshes(test_mesh, 5000) loss_chamfer, _ = chamfer_distance(sample_sphere, sample_test)