Stereo matching with space-constrained cost aggregation and segmentation-based disparity refinement

摘要

Stereo matching is a fundamental topic in computer vision. Usually, stereo matching is mainly composed of four stages: cost computation, cost aggregation, disparity optimization and disparity refinement. In this paper, we propose a novel stereo matching method with space-constrained cost aggregation and segmentation-based disparity refinement. State-of-the-art methods are used for cost aggregation and disparity optimization stages. Three technical contributions are given in this paper. First, applying space-constrained cross-region in cost aggregation stage; second, utilizing both color and disparity information in image segmentation; third, using image segmentation and occlusion region detection to aid disparity refinement. The performance of our platform ranks second in the Middlebury evaluation.