meshes_io - PyTorch3D
pytorch3d.org › docs › meshes_ioThe Meshes object represents a batch of triangulated meshes, and is central to much of the functionality of PyTorch3D. There is no insistence that each mesh in the batch has the same number of vertices or faces. When available, it can store other data which pertains to the mesh, for example face normals, face areas and textures.
PyTorch3D · A library for deep learning with 3D data
pytorch3d.orgSupports batching of 3D inputs of different sizes such as meshes Fast 3D Operators Supports optimized implementations of several common functions for 3D data Differentiable Rendering Modular differentiable rendering API with parallel implementations in PyTorch, C++ and CUDA Get Started Install PyTorch3D (following the instructions here)
PyTorch3D · A library for deep learning with 3D data
https://pytorch3d.orgInstall 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 ...
meshes_io - PyTorch3D
https://pytorch3d.org/docs/meshes_ioThe Meshes object represents a batch of triangulated meshes, and is central to much of the functionality of PyTorch3D. There is no insistence that each mesh in the batch has the same number of vertices or faces. When available, it can store other data which pertains to the mesh, for example face normals, face areas and textures.