Global optimality for point set registration using semidefinite programming

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Bibtex

@inproceedings{5b60b4a02fe94bc39b01a87acea4bbf8,
title = "Global optimality for point set registration using semidefinite programming",
abstract = "In this paper we present a study of global optimality conditions for Point Set Registration (PSR) with missing data. PSR is the problem of aligning multiple point clouds with an unknown target point cloud. Since non-linear rotation constraints are present the problem is inherently non-convex and typically relaxed by computing the Lagrange dual, which is a Semidefinite Program (SDP). In this work we show that given a local minimizer the dual variables of the SDP can be computed in closed form. This opens up the possibility of verifying the optimally, using the SDP formulation without explicitly solving it. In addition it allows us to study under what conditions the relaxation is tight, through spectral analysis. We show that if the errors in the (unknown) optimal solution are bounded the SDP formulation will be able to recover it.",
author = "Iglesias, {Jos{\'e} Pedro} and Carl Olsson and Fredrik Kahl",
year = "2020",
doi = "10.1109/CVPR42600.2020.00831",
language = "English",
pages = "8284--8292",
booktitle = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
note = "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 ; Conference date: 14-06-2020 Through 19-06-2020",

}