Implementing an Adaptive Approach for Dense Stereo-matching

Defining pixel correspondences in stereo-pairs is a fundamental process in automated image-based 3D reconstruction. In this contribution we report on an approach for dense matching, based on local optimization. The approach represents a fusion of state-of-theart algorithms and novel considerations, which mainly involve improvements in the cost computation and aggregation processes. The matching cost which has been implemented here combines the absolute difference of image colour values with a census transformation directly on images gradient of all colour channels. Besides, a new cost volume is computed by aggregating over cross-window support regions with a linearly defined threshold on cross-window expansion. Aggregated costs are, then, refined using a scan-line optimization technique, and the disparity map is estimated using a ‘winner-takes-all’ selection. Occlusions and mismatches are also handled using existing schemes. The proposed algorithm is tested on a standard stereo-matching data-set with promising results. The future tasks mainly include research on refinement of the disparity map and development of a self-adaptive approach for weighting the contribution of different matching cost components.