Tangent Space Backpropagation for 3D Transformation Groups
@article{Teed2021TangentSB, title={Tangent Space Backpropagation for 3D Transformation Groups}, author={Zachary Teed and Jia Deng}, journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2021}, pages={10333-10342} }
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…
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References
SHOWING 1-10 OF 42 REFERENCES
Manopt, a matlab toolbox for optimization on manifolds
- Computer ScienceJ. Mach. Learn. Res.
- 2014
The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms, which aims particularly at lowering the entrance barrier.
Manifold Geometry with Fast Automatic Derivatives and Coordinate Frame Semantics Checking in C++
- Computer Science2018 15th Conference on Computer and Robot Vision (CRV)
- 2018
Computer vision and robotics problems often require representation and estimation of poses on the SE(3) manifold. Developers of algorithms that must run in real time face several time-consuming…
Initialization techniques for 3D SLAM: A survey on rotation estimation and its use in pose graph optimization
- Computer Science2015 IEEE International Conference on Robotics and Automation (ICRA)
- 2015
It is shown that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.
SuperGlue: Learning Feature Matching With Graph Neural Networks
- Computer Science2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2020
SuperGlue is introduced, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points and introduces a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly.
A micro Lie theory for state estimation in robotics
- MathematicsArXiv
- 2018
This paper will walk through the most basic principles of the Lie theory, with the aim of conveying clear and useful ideas, and leave a significant corpus of theLie theory behind.
g 2 o: A general Framework for (Hyper) Graph Optimization
- Computer Science
- 2011
A C++ framework for performing the optimization of nonlinear least squares problems that can be embedded as a graph or in an hyper-graph, where go stands for General (Hyper) Graph Optimization.
Distributed 3-D Localization of Camera Sensor Networks From 2-D Image Measurements
- Computer ScienceIEEE Transactions on Automatic Control
- 2014
Distributed algorithms that use 2-D image measurements to estimate the absolute 3-D poses of the nodes in a camera network, with the purpose of enabling higher-level tasks such as tracking and recognition are proposed.
Factor Graphs and GTSAM: A Hands-on Introduction
- Computer Science
- 2012
This document provides a hands-on introduction to both factor graphs and GTSAM, a BSD-licensed C++ library based on factor graphs developed at the Georgia Institute of Technology by myself, many of my students, and collaborators.
SE3-nets: Learning rigid body motion using deep neural networks
- Computer Science2017 IEEE International Conference on Robotics and Automation (ICRA)
- 2017
SE3-Nets which are deep neural networks designed to model and learn rigid body motion from raw point cloud data and predict SE(3) transformations for different parts of the scene are introduced.
DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
- Computer ScienceIEEE Robotics and Automation Letters
- 2020
A SLAM system that unifies these methods in a probabilistic framework while still maintaining real-time performance through the use of a learned compact depth map representation and reformulating three different types of errors: photometric, reprojection and geometric.