EFFICIENT BP STEREO WITH AUTOMATIC PARAMEMETER ESTIMATION

In this paper, we propose a series of techniques to enhance the computational performance of existing Belief Propagation (BP) based stereo matching that relies on automatic estimation of the Markov random field (MRF) parameters. First, we show how convergence in matching can be achieved faster than with the existing message comparison technique by skipping comparisons in early inferences. Second, assuming that a stereo pair is captured with identical cameras, we apply a hypothesis called noise equivalence to pre-estimate the likelihood parameters and thus, avoid costly nested inferences to reduce the computational time. The likelihood parameters and intensity information are used for accelerated message propagation in image regions lacking gradients. Third, the prior model parameters are estimated with a combination of maximum likelihood (ML) estimation and disparity gradient constraint to further reduce the computational time. Supporting experiments for the proposed algorithms show encouraging results on ground truth test images