103da63393
Summary: Implementation of ball query from PointNet++. This function is similar to KNN (find the neighbors in p2 for all points in p1). These are the key differences: - It will return the **first** K neighbors within a specified radius as opposed to the **closest** K neighbors. - As all the points in p2 do not need to be considered to find the closest K, the algorithm is much faster than KNN when p2 has a large number of points. - The neighbors are not sorted - Due to the radius threshold it is not guaranteed that there will be K neighbors even if there are more than K points in p2. - The padding value for `idx` is -1 instead of 0. # Note: - Some of the code is very similar to KNN so it could be possible to modify the KNN forward kernels to support ball query. - Some users might want to use kNN with ball query - for this we could provide a wrapper function around the current `knn_points` which enables applying the radius threshold afterwards as an alternative. This could be called `ball_query_knn`. Reviewed By: jcjohnson Differential Revision: D30261362 fbshipit-source-id: 66b6a7e0114beff7164daf7eba21546ff41ec450
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 License.
Tutorials
Get started with PyTorch3D by trying one of the tutorial notebooks.
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| Deform a sphere mesh to dolphin | Bundle adjustment |
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| Render textured meshes | Camera position optimization |
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| Render textured pointclouds | Fit a mesh with texture |
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| Render DensePose data | Load & Render ShapeNet data |
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| Fit Textured Volume | Fit A Simple Neural Radiance Field |
Documentation
Learn more about the API by reading the PyTorch3D documentation.
We also have deep dive notes on several API components:
Overview Video
We have created a short (~14 min) video tutorial providing an overview of the PyTorch3D codebase including several code examples. Click on the image below to watch the video on YouTube:
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.
In alphabetical order:
- Amitav Baruah
- Steve Branson
- Luya Gao
- Georgia Gkioxari
- Taylor Gordon
- Justin Johnson
- Patrick Labtut
- Christoph Lassner
- Wan-Yen Lo
- David Novotny
- Nikhila Ravi
- Jeremy Reizenstein
- Dave Schnizlein
- Roman Shapovalov
- Olivia Wiles
Citation
If you find PyTorch3D useful in your research, please cite our tech report:
@article{ravi2020pytorch3d,
author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
title = {Accelerating 3D Deep Learning with PyTorch3D},
journal = {arXiv:2007.08501},
year = {2020},
}
If you are using the pulsar backend for sphere-rendering (the PulsarPointRenderer or pytorch3d.renderer.points.pulsar.Renderer), please cite the tech report:
@article{lassner2020pulsar,
author = {Christoph Lassner and Michael Zollh\"ofer},
title = {Pulsar: Efficient Sphere-based Neural Rendering},
journal = {arXiv:2004.07484},
year = {2020},
}
News
Please see below for a timeline of the codebase updates in reverse chronological order. We are sharing updates on the releases as well as research projects which are built with PyTorch3D. The changelogs for the releases are available under Releases, and the builds can be installed using conda as per the instructions in INSTALL.md.
[Feb 9th 2021]: PyTorch3D v0.4.0 released with support for implicit functions, volume rendering and a reimplementation of NeRF.
[November 2nd 2020]: PyTorch3D v0.3.0 released, integrating the pulsar backend.
[Aug 28th 2020]: PyTorch3D v0.2.5 released
[July 17th 2020]: PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501
[April 24th 2020]: PyTorch3D v0.2.0 released
[March 25th 2020]: SynSin codebase released using PyTorch3D: https://github.com/facebookresearch/synsin
[March 8th 2020]: PyTorch3D v0.1.1 bug fix release
[Jan 23rd 2020]: PyTorch3D v0.1.0 released. Mesh R-CNN codebase released: https://github.com/facebookresearch/meshrcnn