Confidence-based hierarchical support window for fast local stereo matching

Abstract

Various cost aggregation methods have been developed for finding correspondences between stereo pairs, but their high complexity is still a problem for practical use. In this paper, we propose a confidence-based hierarchical structure to reduce the complexity of the cost aggregation algorithms. Aggregating matching costs for each pixel with the smallest support window, we estimate confidence levels. The confidence values are used to decide which pixel needs additional cost aggregations. For the pixels of small confidence, we iteratively supplement their matching costs by using larger support windows. Our experiments show that our approach reduces computational time and improves the quality of output disparity images.