Patch warping and local constraints for improved block matching stereo correspondence

Depth estimation of the surrounding environment using a stereoscopic camera setup is an important and fundamental research topic in computer vision. Due to its running time and quality performance in real situations the semi global matching algorithm is often used. The biggest disadvantage of the semi global approach is its large memory footprint. On the other hand, block matching stereo is leaner when it comes to memory consumption and therefore it is commonly used in applications where we do not have many resources, in order to obtain coarse depth information of the environment. The poor quality performance of such algorithms make them impractical for many real life applications. In this paper we focus on improving the quality of the classical block matching (BM) stereo method by proposing a novel approach which tackles the problem of stereo matching for slanted and fronto-parallel surfaces by using different types of binary masks on the matching window. Another improvement consists in the usage of different types of local constraints in the generation of the winning disparity for a specific position, such that possible outliers are eliminated from the start. The validation of our results has been done on the KITTI stereo benchmark dataset.