Hierarchical depth processing with adaptive search range and fusion

Abstract

In this paper, we present an effective hierarchical depth processing and fusion for large stereo images. We propose the adaptive disparity search range based on the combined local structure from image and initial disparity. The adaptive search range can propagate the smoothness property at the coarse level to the fine level while preserving details and suppressing undesirable errors. The spatial-multiscale total variation method is investigated to enforce the spatial and scaling consistency of multi-scale depth estimates. The experimental results demonstrate that the proposed hierarchical scheme produces high quality and high resolution depth maps by fusing individual multi-scale depth maps, while reducing complexity.