Multi-cut light field depth estimation

We investigate the problem of depth map estimation of a scene from 4D light-field data. Unlike prior work, we process all the input images (or sub-images) of the light-field. Indeed, for each point of the scene, different depth candidates are estimated by considering all possible 3D light-field cuts instead of only 2, i.e. the horizontal and vertical one. Then, the optimal candidates are chosen by finding the labeling with minimum energy. The main motivation of our approach is that by processing multiple cuts of the light-field, the matching ambiguities are reduced. Our meta-method is of broad interest as it can be applied and enhance the performance of various state-of-the-art light-field depth estimation techniques. For the sake of demonstration we apply it on the work from Wanner and Goldluecke [19] and significantly improve their results.