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29cd181a83
Summary: Added cpu implementation for face areas normals. Moved test and bm to separate functions. ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- FACE_AREAS_NORMALS_2_100_300_False 196 268 2550 FACE_AREAS_NORMALS_2_100_300_True 106 179 4733 FACE_AREAS_NORMALS_2_100_3000_False 1447 1630 346 FACE_AREAS_NORMALS_2_100_3000_True 107 178 4674 FACE_AREAS_NORMALS_2_1000_300_False 201 309 2486 FACE_AREAS_NORMALS_2_1000_300_True 107 186 4673 FACE_AREAS_NORMALS_2_1000_3000_False 1451 1636 345 FACE_AREAS_NORMALS_2_1000_3000_True 107 186 4655 FACE_AREAS_NORMALS_10_100_300_False 767 918 653 FACE_AREAS_NORMALS_10_100_300_True 106 167 4712 FACE_AREAS_NORMALS_10_100_3000_False 7036 7754 72 FACE_AREAS_NORMALS_10_100_3000_True 113 164 4445 FACE_AREAS_NORMALS_10_1000_300_False 748 947 669 FACE_AREAS_NORMALS_10_1000_300_True 108 169 4638 FACE_AREAS_NORMALS_10_1000_3000_False 7069 7783 71 FACE_AREAS_NORMALS_10_1000_3000_True 108 172 4646 FACE_AREAS_NORMALS_32_100_300_False 2286 2496 219 FACE_AREAS_NORMALS_32_100_300_True 108 180 4631 FACE_AREAS_NORMALS_32_100_3000_False 23184 24369 22 FACE_AREAS_NORMALS_32_100_3000_True 159 213 3147 FACE_AREAS_NORMALS_32_1000_300_False 2414 2645 208 FACE_AREAS_NORMALS_32_1000_300_True 112 197 4480 FACE_AREAS_NORMALS_32_1000_3000_False 21687 22964 24 FACE_AREAS_NORMALS_32_1000_3000_True 141 211 3540 -------------------------------------------------------------------------------- Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- FACE_AREAS_NORMALS_TORCH_2_100_300_False 5465 5782 92 FACE_AREAS_NORMALS_TORCH_2_100_300_True 1198 1351 418 FACE_AREAS_NORMALS_TORCH_2_100_3000_False 48228 48869 11 FACE_AREAS_NORMALS_TORCH_2_100_3000_True 1186 1304 422 FACE_AREAS_NORMALS_TORCH_2_1000_300_False 5556 6097 90 FACE_AREAS_NORMALS_TORCH_2_1000_300_True 1200 1328 417 FACE_AREAS_NORMALS_TORCH_2_1000_3000_False 48683 50016 11 FACE_AREAS_NORMALS_TORCH_2_1000_3000_True 1185 1306 422 FACE_AREAS_NORMALS_TORCH_10_100_300_False 24215 25097 21 FACE_AREAS_NORMALS_TORCH_10_100_300_True 1150 1314 435 FACE_AREAS_NORMALS_TORCH_10_100_3000_False 232605 234952 3 FACE_AREAS_NORMALS_TORCH_10_100_3000_True 1193 1314 420 FACE_AREAS_NORMALS_TORCH_10_1000_300_False 24912 25343 21 FACE_AREAS_NORMALS_TORCH_10_1000_300_True 1216 1330 412 FACE_AREAS_NORMALS_TORCH_10_1000_3000_False 239907 241253 3 FACE_AREAS_NORMALS_TORCH_10_1000_3000_True 1226 1333 408 FACE_AREAS_NORMALS_TORCH_32_100_300_False 73991 75776 7 FACE_AREAS_NORMALS_TORCH_32_100_300_True 1193 1339 420 FACE_AREAS_NORMALS_TORCH_32_100_3000_False 728932 728932 1 FACE_AREAS_NORMALS_TORCH_32_100_3000_True 1186 1359 422 FACE_AREAS_NORMALS_TORCH_32_1000_300_False 76385 79129 7 FACE_AREAS_NORMALS_TORCH_32_1000_300_True 1165 1310 430 FACE_AREAS_NORMALS_TORCH_32_1000_3000_False 753276 753276 1 FACE_AREAS_NORMALS_TORCH_32_1000_3000_True 1205 1340 415 -------------------------------------------------------------------------------- ``` Reviewed By: bottler, jcjohnson Differential Revision: D19864385 fbshipit-source-id: 3a87ae41a8e3ab5560febcb94961798f2e09dfb8
Introduction
PyTorch3d provides efficient, reusable components for 3D Computer Vision research with PyTorch.
Key features include:
- Data structure for storing and manipulating triangle meshes
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
- A differentiable mesh renderer
PyTorch3d is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in PyTorch3d:
- Are implemented using PyTorch tensors
- Can handle minibatches of hetereogenous data
- Can be differentiated
- Can utilize GPUs for acceleration
Within FAIR, PyTorch3d has been used to power research projects such as Mesh R-CNN.
Installation
For detailed instructions refer to INSTALL.md.
License
PyTorch3d is released under the BSD-3-Clause License.
Tutorials
Get started with PyTorch3d by trying one of the tutorial notebooks.
 |
 |
|---|---|
| Deform a sphere mesh to dolphin | Bundle adjustment |
 |
 |
|---|---|
| Render textured meshes | Camera position optimization |
Documentation
Learn more about the API by reading the PyTorch3d documentation.
We also have deep dive notes on several API components:
Development
We welcome new contributions to Pytorch3d and we will be actively maintaining this library! Please refer to CONTRIBUTING.md for full instructions on how to run the code, tests and linter, and submit your pull requests.
Contributors
PyTorch3d is written and maintained by the Facebook AI Research Computer Vision Team.
Citation
If you find PyTorch3d useful in your research, please cite:
@misc{ravi2020pytorch3d,
author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
title = {PyTorch3D},
howpublished = {\url{https://github.com/facebookresearch/pytorch3d}},
year = {2020}
}