Tangent Space Backpropagation for 3D Transformation Groups
Zachary Teed, Jia Deng
Abstract
We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in 3D vision and robotics, but they do not form vector spaces and instead lie on smooth manifolds. The standard backpropagation approach, which embeds 3D transformations in Euclidean spaces, suffers from numerical difficulties. We introduce a new library, which exploits the group structure of 3D transformations and performs backpropagation in the tangent spaces of manifolds. We show that our approach is numerically more stable, easier to implement, and beneficial to a diverse set of tasks. Our plug-and-play PyTorch library is available at https: //github.com/princeton-vl/lietorch .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers16
- DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D CamerasZachary Teed, Jia DengNeurIPS 2021 · 1,248 citations
- NICE-SLAM: Neural Implicit Scalable Encoding for SLAMZihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu et al.CVPR 2022 · 720 citations
- Theseus: A Library for Differentiable Nonlinear OptimizationLuis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman et al.NeurIPS 2022 · 124 citations
- Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual LearningYouqi Pan, Wugen Zhou, Yingdian Cao, Hongbin ZhaCVPR 2024 · 17 citations
- Projective Manifold Gradient Layer for Deep Rotation RegressionJiayi Chen, Yingda Yin, Tolga Birdal, Baoquan Chen et al.CVPR 2022 · 16 citations
Builds on5
- Consistent video depth estimationXuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen et al.SIGGRAPH 2020 · 321 citations
- DeepV2D: Video to Depth with Differentiable Structure from MotionZachary Teed, Jia DengICLR 2020 · 314 citations
- SuperGlue: Learning Feature Matching With Graph Neural NetworksPaul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew RabinovichCVPR 2020
- Deep Global RegistrationChristopher B. Choy, Wei Dong, Vladlen KoltunCVPR 2020
- Learning Multiview 3D Point Cloud RegistrationZan Gojcic, Caifa Zhou, Jan D. Wegner, Leonidas J. Guibas et al.CVPR 2020
Related papers
- Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose ModelingOlaf Dünkel, Tim Salzmann, Florian PfaffCVPR 2024
- Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation ModelingYulin Liu, Haoran Liu, Yingda Yin, Yang Wang et al.CVPR 2023
- A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix GroupsMarc Finzi, Max Welling, Andrew Gordon WilsonICML 2021 · 226 citations
- Stochastic Flows and Geometric Optimization on the Orthogonal GroupKrzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov et al.ICML 2020 · 7 citations
- A Primer on SO(3) Action Representations in Deep Reinforcement LearningMartin Schuck, Sherif Samy, Angela P. SchoelligICLR 2026 · 2 citations
