Deterministic Consensus Maximization with Biconvex Programming

Zhipeng Cai, Tat-Jun Chin, Huu Le, David Suter; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 685-700


Consensus maximization is one of the most widely used robust fitting paradigms in computer vision, and the development of algorithms for consensus maximization is an active research topic. In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization. Given an initial solution, our method conducts a deterministic search that forcibly increases the consensus of the initial solution. We show how each iteration of the update can be formulated as an instance of biconvex programming, which we solve efficiently using a novel biconvex optimization algorithm. In contrast to our algorithm, previous consensus improvement techniques rely on random sampling or relaxations of the objective function, which reduce their ability to significantly improve the initial consensus. In fact, on challenging instances, the previous techniques may even return a worse off solution. Comprehensive experiments show that our algorithm can consistently and greatly improve the quality of the initial solution, without substantial cost.

Related Material

[pdf] [arXiv]
author = {Cai, Zhipeng and Chin, Tat-Jun and Le, Huu and Suter, David},
title = {Deterministic Consensus Maximization with Biconvex Programming},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}