mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-03 04:12:48 +08:00
25d2e2c8b7
Summary: Use a consistent case for PyTorch3D (matching the logo...): replace all occurrences of PyTorch3d with PyTorch3D across the codebase (including documentation and notebooks) Reviewed By: wanyenlo, gkioxari Differential Revision: D20427546 fbshipit-source-id: 8c7697f51434c51e99b7fe271935932c72a1d9b9
14 lines
889 B
Markdown
14 lines
889 B
Markdown
---
|
|
hide_title: true
|
|
sidebar_label: Why PyTorch3D
|
|
---
|
|
|
|
|
|
# Why PyTorch3D
|
|
|
|
|
|
Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as [Mesh R-CNN](https://github.com/facebookresearch/meshrcnn) and [C3DPO](https://github.com/facebookresearch/c3dpo_nrsfm), we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area.
|
|
|
|
In PyTorch3D we have included efficient 3D operators, heterogeneous batching capabilities, and a modular differentiable rendering API, to equip researchers in this field with a much needed toolkit to implement cutting-edge research with complex 3D inputs.
|
|
|