Stereo Matching Based on Features of Image Patch

Abstract

Stereo matching is a branch of 3D vision and has a wide range of applications in 3D reconstruction and autonomous driving. Recently, stereo matching methods leverage all the information of the stereo image to calculate a disparity map. However, these methods still have difficulties in texture-less areas and occlusion areas, and post-processing need to do to improve the accuracy. Therefore, there is a high computational cost in feature extraction and post-processing. This paper proposes a stereo matching method that predict the disparity of non-occlusion areas with corresponding features, instead of predicting the disparity of the full image with all features. And aggregation methods are performed to modify all kinds of mismatching pixels based on the correct disparity in the non-occlusion areas. Furthermore, we evaluated the proposed method on the Middlebury dataset. The result shows that the proposed method performs well in all areas.