Stereo Matching Using Population-Based MCMC

Abstract

In this paper, we propose a new stereo matching method using the population-based Markov Chain Monte Carlo (Pop-MCMC), which belongs to the sampling-based methods. Since the previous MCMC methods produce only one sample at a time, only local moves are available. In contrast, the proposed Pop-MCMC uses multiple chains in parallel and produces multiple samples at a time. It thereby enables global moves by exchanging information between samples, which in turn, leads to faster mixing rate. In the view of optimization, it means that we can reach a lower energy state rapidly. In order to apply Pop-MCMC to the stereo matching problem, we design two effective 2-D mutation and crossover moves among multiple chains to explore a high dimensional state space efficiently. The experimental results on real stereo images demonstrate that the proposed algorithm gives much faster convergence rate than conventional sampling-based methods including SA (Simulated Annealing) and SWC (Swendsen-Wang Cuts). And it also gives consistently lower energy solutions than BP (Belief Propagation) in our experiments. In addition, we also analyze the effect of each move in Pop-MCMC and examine the effect of parameters such as temperature and the number of the chains.