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Summary: Umeyama estimates a rigid motion between two sets of corresponding points. Benchmark output for `bm_points_alignment` ``` Arguments key: [<allow_reflection>_<batch_size>_<dim>_<estimate_scale>_<n_points>_<use_pointclouds>] Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- CorrespodingPointsAlignment_True_1_3_True_100_False 7382 9833 68 CorrespodingPointsAlignment_True_1_3_True_10000_False 8183 10500 62 CorrespodingPointsAlignment_True_1_3_False_100_False 7301 9263 69 CorrespodingPointsAlignment_True_1_3_False_10000_False 7945 9746 64 CorrespodingPointsAlignment_True_1_20_True_100_False 13706 41623 37 CorrespodingPointsAlignment_True_1_20_True_10000_False 11044 33766 46 CorrespodingPointsAlignment_True_1_20_False_100_False 9908 28791 51 CorrespodingPointsAlignment_True_1_20_False_10000_False 9523 18680 53 CorrespodingPointsAlignment_True_10_3_True_100_False 29585 32026 17 CorrespodingPointsAlignment_True_10_3_True_10000_False 29626 36324 18 CorrespodingPointsAlignment_True_10_3_False_100_False 26013 29253 20 CorrespodingPointsAlignment_True_10_3_False_10000_False 25000 33820 20 CorrespodingPointsAlignment_True_10_20_True_100_False 40955 41592 13 CorrespodingPointsAlignment_True_10_20_True_10000_False 42087 42393 12 CorrespodingPointsAlignment_True_10_20_False_100_False 39863 40381 13 CorrespodingPointsAlignment_True_10_20_False_10000_False 40813 41699 13 CorrespodingPointsAlignment_True_100_3_True_100_False 183146 194745 3 CorrespodingPointsAlignment_True_100_3_True_10000_False 213789 231466 3 CorrespodingPointsAlignment_True_100_3_False_100_False 177805 180796 3 CorrespodingPointsAlignment_True_100_3_False_10000_False 184963 185695 3 CorrespodingPointsAlignment_True_100_20_True_100_False 347181 347325 2 CorrespodingPointsAlignment_True_100_20_True_10000_False 363259 363613 2 CorrespodingPointsAlignment_True_100_20_False_100_False 351769 352496 2 CorrespodingPointsAlignment_True_100_20_False_10000_False 375629 379818 2 CorrespodingPointsAlignment_False_1_3_True_100_False 11155 13770 45 CorrespodingPointsAlignment_False_1_3_True_10000_False 10743 13938 47 CorrespodingPointsAlignment_False_1_3_False_100_False 9578 11511 53 CorrespodingPointsAlignment_False_1_3_False_10000_False 9549 11984 53 CorrespodingPointsAlignment_False_1_20_True_100_False 13809 14183 37 CorrespodingPointsAlignment_False_1_20_True_10000_False 14084 15082 36 CorrespodingPointsAlignment_False_1_20_False_100_False 12765 14177 40 CorrespodingPointsAlignment_False_1_20_False_10000_False 12811 13096 40 CorrespodingPointsAlignment_False_10_3_True_100_False 28823 39384 18 CorrespodingPointsAlignment_False_10_3_True_10000_False 27135 27525 19 CorrespodingPointsAlignment_False_10_3_False_100_False 26236 28980 20 CorrespodingPointsAlignment_False_10_3_False_10000_False 42324 45123 12 CorrespodingPointsAlignment_False_10_20_True_100_False 723902 723902 1 CorrespodingPointsAlignment_False_10_20_True_10000_False 220007 252886 3 CorrespodingPointsAlignment_False_10_20_False_100_False 55593 71636 9 CorrespodingPointsAlignment_False_10_20_False_10000_False 44419 71861 12 CorrespodingPointsAlignment_False_100_3_True_100_False 184768 185199 3 CorrespodingPointsAlignment_False_100_3_True_10000_False 198657 213868 3 CorrespodingPointsAlignment_False_100_3_False_100_False 224598 309645 3 CorrespodingPointsAlignment_False_100_3_False_10000_False 197863 202002 3 CorrespodingPointsAlignment_False_100_20_True_100_False 293484 309459 2 CorrespodingPointsAlignment_False_100_20_True_10000_False 327253 366644 2 CorrespodingPointsAlignment_False_100_20_False_100_False 420793 422194 2 CorrespodingPointsAlignment_False_100_20_False_10000_False 462634 485542 2 CorrespodingPointsAlignment_True_1_3_True_100_True 7664 9909 66 CorrespodingPointsAlignment_True_1_3_True_10000_True 7190 8366 70 CorrespodingPointsAlignment_True_1_3_False_100_True 6549 8316 77 CorrespodingPointsAlignment_True_1_3_False_10000_True 6534 7710 77 CorrespodingPointsAlignment_True_10_3_True_100_True 29052 32940 18 CorrespodingPointsAlignment_True_10_3_True_10000_True 30526 33453 17 CorrespodingPointsAlignment_True_10_3_False_100_True 28708 32993 18 CorrespodingPointsAlignment_True_10_3_False_10000_True 30630 35973 17 CorrespodingPointsAlignment_True_100_3_True_100_True 264909 320820 3 CorrespodingPointsAlignment_True_100_3_True_10000_True 310902 322604 2 CorrespodingPointsAlignment_True_100_3_False_100_True 246832 250634 3 CorrespodingPointsAlignment_True_100_3_False_10000_True 276006 289061 2 CorrespodingPointsAlignment_False_1_3_True_100_True 11421 13757 44 CorrespodingPointsAlignment_False_1_3_True_10000_True 11199 12532 45 CorrespodingPointsAlignment_False_1_3_False_100_True 11474 15841 44 CorrespodingPointsAlignment_False_1_3_False_10000_True 10384 13188 49 CorrespodingPointsAlignment_False_10_3_True_100_True 36599 47340 14 CorrespodingPointsAlignment_False_10_3_True_10000_True 40702 50754 13 CorrespodingPointsAlignment_False_10_3_False_100_True 41277 52149 13 CorrespodingPointsAlignment_False_10_3_False_10000_True 34286 37091 15 CorrespodingPointsAlignment_False_100_3_True_100_True 254991 258578 2 CorrespodingPointsAlignment_False_100_3_True_10000_True 257999 261285 2 CorrespodingPointsAlignment_False_100_3_False_100_True 247511 248693 3 CorrespodingPointsAlignment_False_100_3_False_10000_True 251807 263865 3 ``` Reviewed By: gkioxari Differential Revision: D19808389 fbshipit-source-id: 83305a58627d2fc5dcaf3c3015132d8148f28c29
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}
}