Dynamical Pose Estimation
Name
2103.06182v3.pdf
Description
Accepted version
Size
7.19 MB
Format
Adobe PDF
Checksum (MD5)
0c30c4e5d8c10bc7610fb85d9cbeaa6e
Author(s)
Yang, Heng
Doran, Chris
Slotine, Jean-Jacques
Date Issued
October 2021
Publisher
IEEE
Citation
H. Yang, C. Doran and J. -J. Slotine, "Dynamical Pose Estimation," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 5906-5915,.
Version
Author's final manuscript
Abstract
We study the problem of aligning two sets of 3D geometric primitives given known correspondences. Our first contribution is to show that this primitive alignment framework unifies five perception problems including point cloud registration, primitive (mesh) registration, category-level 3D registration, absolution pose estimation (APE), and category-level APE. Our second contribution is to propose DynAMical Pose estimation (DAMP), the first general and practical algorithm to solve primitive alignment problem by simulating rigid body dynamics arising from virtual springs and damping, where the springs span the shortest distances between corresponding primitives. We evaluate DAMP in simulated and real datasets across all five problems, and demonstrate (i) DAMP always converges to the globally optimal solution in the first three problems with 3D-3D correspondences; (ii) although DAMP sometimes converges to suboptimal solutions in the last two problems with 2D-3D correspondences, using a scheme for escaping local minima, DAMP always succeeds. Our third contribution is to demystify the surprising empirical performance of DAMP and formally prove a global convergence result in the case of point cloud registration by charactering local stability of the equilibrium points of the underlying dynamical system.
Description
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 10-17 October 2021, Montreal, QC, Canada
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iccv48922.2021.00587
