  In this work, we explore disparity estimation from a high number of views. We
experimentally identify occlusions as a key challenge for disparity estimation
for applications with high numbers of views. In particular, occlusions can
actually result in a degradation in accuracy as more views are added to a
dataset. We propose the use of a Welsch loss function for the data term in a
global variational framework for disparity estimation. We also propose a
disciplined warping strategy and a progressive inclusion of views strategy that
can reduce the need for coarse to fine strategies that discard high spatial
frequency components from the early iterations. Experimental results
demonstrate that the proposed approach produces superior and/or more robust
estimates than other conventional variational approaches.
