Deterministic approximate methods for maximum consensus robust fitting
Document Type
Journal Article
Publication Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
43
Issue
3
First Page
842
Last Page
857
Publisher
IEEE
School
School of Science
RAS ID
30758
Funders
ARC Discovery Project ARC Future Fellowships ARC Centres of Excellence
Grant Number
ARC Number : DP160103490, FT170100072, CE140100016
Grant Link
http://purl.org/au-research/grants/arc/DP160103490 http://purl.org/au-research/grants/arc/FT170100072 http://purl.org/au-research/grants/arc/CE140100016
Abstract
© 1979-2012 IEEE. Maximum consensus estimation plays a critically important role in several robust fitting problems in computer vision. Currently, the most prevalent algorithms for consensus maximization draw from the class of randomized hypothesize-and-verify algorithms, which are cheap but can usually deliver only rough approximate solutions. On the other extreme, there are exact algorithms which are exhaustive search in nature and can be costly for practical-sized inputs. This paper fills the gap between the two extremes by proposing deterministic algorithms to approximately optimize the maximum consensus criterion. Our work begins by reformulating consensus maximization with linear complementarity constraints. Then, we develop two novel algorithms: one based on non-smooth penalty method with a Frank-Wolfe style optimization scheme, the other based on the Alternating Direction Method of Multipliers (ADMM). Both algorithms solve convex subproblems to efficiently perform the optimization. We demonstrate the capability of our algorithms to greatly improve a rough initial estimate, such as those obtained using least squares or a randomized algorithm. Compared to the exact algorithms, our approach is much more practical on realistic input sizes. Further, our approach is naturally applicable to estimation problems with geometric residuals. Matlab code and demo program for our methods can be downloaded from https://goo.gl/FQcxpi.
DOI
10.1109/TPAMI.2019.2939307
Access Rights
subscription content
Comments
Le, H. M., Chin, T. J., Eriksson, A., Do, T. T., & Suter, D. (2021). Deterministic approximate methods for maximum consensus robust fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 842-857. https://doi.org/10.1109/TPAMI.2019.2939307