Least-squares registration of point sets over SE(d) using closed-form projections

Ahmed, Sk Miraj and Das, Niladri Ranjan and Chaudhury, Kunal Narayan (2019) Least-squares registration of point sets over SE(d) using closed-form projections. In: COMPUTER VISION AND IMAGE UNDERSTANDING, 183 . pp. 20-32.

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Abstract

Consider the problem of registering multiple point sets in some d-dimensional space using rotations and translations. Assume that there are sets with common points, and moreover the pairwise correspondences are known for such sets. We consider a least-squares formulation of this problem, where the variables are the transforms associated with the point sets. The present novelty is that we reduce this nonconvex problem to an optimization over the positive semidefinite cone, where the objective is linear but the constraints are nevertheless nonconvex. We propose to solve this using variable splitting and the alternating directions method of multipliers (ADMM). Due to the linearity of the objective and the structure of constraints, the ADMM subproblems are given by projections with closed-form solutions. In particular, for m point sets, the dominant cost per iteration is the partial eigendecomposition of an and x and matrix, and m - 1 singular value decompositions of d x d matrices. We empirically show that for appropriate parameter settings, the proposed solver has a large convergence basin and is stable under perturbations. As applications, we use our method for 2D shape matching and 3D multiview registration. In either application, we model the shapes/scans as point sets and determine the pairwise correspondences using ICP. In particular, our algorithm compares favorably with existing methods for multiview reconstruction in terms of timing and accuracy.

Item Type:	Journal Article
Publication:	COMPUTER VISION AND IMAGE UNDERSTANDING
Publisher:	ACADEMIC PRESS INC ELSEVIER SCIENCE
Additional Information:	copyright for this article belongs to COMPUTER VISION AND IMAGE UNDERSTANDING
Keywords:	Registration; Semidefinite programming; ADMM
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	25 Jun 2019 19:00
Last Modified:	25 Jun 2019 19:00
URI:	http://eprints.iisc.ac.in/id/eprint/63046

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