David Novotny 8abbe22ffb ICP - point-to-point version
Summary:
The iterative closest point algorithm - point-to-point version.

Output of `bm_iterative_closest_point`:
Argument key: `batch_size dim n_points_X n_points_Y use_pointclouds`

```
Benchmark                                         Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
IterativeClosestPoint_1_3_100_100_False              107569          111323              5
IterativeClosestPoint_1_3_100_1000_False             118972          122306              5
IterativeClosestPoint_1_3_1000_100_False             108576          110978              5
IterativeClosestPoint_1_3_1000_1000_False            331836          333515              2
IterativeClosestPoint_1_20_100_100_False             134387          137842              4
IterativeClosestPoint_1_20_100_1000_False            149218          153405              4
IterativeClosestPoint_1_20_1000_100_False            414248          416595              2
IterativeClosestPoint_1_20_1000_1000_False           374318          374662              2
IterativeClosestPoint_10_3_100_100_False             539852          539852              1
IterativeClosestPoint_10_3_100_1000_False            752784          752784              1
IterativeClosestPoint_10_3_1000_100_False           1070700         1070700              1
IterativeClosestPoint_10_3_1000_1000_False          1164020         1164020              1
IterativeClosestPoint_10_20_100_100_False            374548          377337              2
IterativeClosestPoint_10_20_100_1000_False           472764          476685              2
IterativeClosestPoint_10_20_1000_100_False          1457175         1457175              1
IterativeClosestPoint_10_20_1000_1000_False         2195820         2195820              1
IterativeClosestPoint_1_3_100_100_True               110084          115824              5
IterativeClosestPoint_1_3_100_1000_True              142728          147696              4
IterativeClosestPoint_1_3_1000_100_True              212966          213966              3
IterativeClosestPoint_1_3_1000_1000_True             369130          375114              2
IterativeClosestPoint_10_3_100_100_True              354615          355179              2
IterativeClosestPoint_10_3_100_1000_True             451815          452704              2
IterativeClosestPoint_10_3_1000_100_True             511833          511833              1
IterativeClosestPoint_10_3_1000_1000_True            798453          798453              1
--------------------------------------------------------------------------------
```

Reviewed By: shapovalov, gkioxari

Differential Revision: D19909952

fbshipit-source-id: f77fadc88fb7c53999909d594114b182ee2a3def
2020-04-16 14:02:16 -07:00
2020-04-07 09:42:31 -07:00
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2020-03-23 09:20:42 -07:00
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2020-03-29 14:51:02 -07:00

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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}
}
Description
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Readme BSD-3-Clause 73 MiB
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