Abstract
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense light fields are not suitable for sparse ones.,,In this paper, we develop an empirical Bayesian framework - Robust Pseudo Random Field - to explore intrinsic statistical cues for broad applicability. Based on pseudo-likelihood, it applies soft expectation-maximization (EM) for good model fitting and hard EM for robust depth estimation. We introduce novel pixel difference models to enable such adaptability and robustness simultaneously. We also devise an algorithm to employ this framework on dense, sparse, and even denoised light fields. Experimental results show that it estimates scene-dependent parameters robustly and converges quickly. In terms of depth accuracy and computation speed, it also outperforms state-of-the-art algorithms constantly.