Increase performance for conversions including axis angles (#1948)

Summary: This is an extension of https://github.com/facebookresearch/pytorch3d/issues/1544 with various speed, stability, and readability improvements. (I could not find a way to make a commit to the existing PR). This PR is still based on the Rodrigues’ rotation formula.

The motivation is the same; this change speeds up the conversions up to 10x, depending on the device, batch size, etc.

Notes

As the angles get very close to π, the existing implementation and the proposed one start to differ. However, (my understanding is that) this is not a problem as the axis can not be stably inferred from the rotation matrix in this case in general.
bottler , I tried to follow similar conventions as existing functions to deal with weird angles, let me know if something needs to be changed to merge this.

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1948

Reviewed By: MichaelRamamonjisoa

Differential Revision: D69193009

Pulled By: bottler

fbshipit-source-id: e5ed34b45b625114ec4419bb89e22a6aefad4eeb

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
Implicitron, see its README, a framework for new-view synthesis via implicit representations. (blog post)

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.

See our blog post to see more demos and learn about PyTorch3D.

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.


Deform a sphere mesh to dolphin	Bundle adjustment


Render textured meshes	Camera position optimization


Render textured pointclouds	Fit a mesh with texture


Render DensePose data	Load & Render ShapeNet data


Fit Textured Volume	Fit A Simple Neural Radiance Field


Fit Textured Volume in Implicitron	Implicitron Config System

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.

Development and Compatibility

main branch: actively developed, without any guarantee, Anything can be broken at any time
- REMARK: this includes nightly builds which are built from main
- HINT: the commit history can help locate regressions or changes
backward-compatibility between releases: no guarantee. Best efforts to communicate breaking changes and facilitate migration of code or data (incl. models).

Contributors

PyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team.

In alphabetical order:

Amitav Baruah
Steve Branson
Krzysztof Chalupka
Jiali Duan
Luya Gao
Georgia Gkioxari
Taylor Gordon
Justin Johnson
Patrick Labatut
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.

[Oct 31st 2023]: PyTorch3D v0.7.5 released.

[May 10th 2023]: PyTorch3D v0.7.4 released.

[Apr 5th 2023]: PyTorch3D v0.7.3 released.

[Dec 19th 2022]: PyTorch3D v0.7.2 released.

[Oct 23rd 2022]: PyTorch3D v0.7.1 released.

[Aug 10th 2022]: PyTorch3D v0.7.0 released with Implicitron and MeshRasterizerOpenGL.

[Apr 28th 2022]: PyTorch3D v0.6.2 released

[Dec 16th 2021]: PyTorch3D v0.6.1 released

[Oct 6th 2021]: PyTorch3D v0.6.0 released

[Aug 5th 2021]: PyTorch3D v0.5.0 released

[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