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.

本文提出了一种高效的确定性优化算法，以增加一组解的最大一致性，该算法将每个迭代的更新公式化为双凸编程实例，并使用新颖的双凸优化算法高效求解。