Gradient guided feature selection in stereo matching

Latest researches in stereo matching mainly focus on disparity optimization. Particularly, much effort has been put into the cost aggregation to improve the cost computation accuracy. However, the performance of the cost aggregation is determined by both the aggregation method and the prior cost volume obtained by the matching cost computation based on certain image features. Different choices of features could result in different quality cost volumes. Therefore, it is necessary to study feature selection for stereo matching. Our experiments show that in the region close to the edges or with high textures, features captured in smaller windows could lead to a better performance. Based on this observation, a novel gradient image guided feature selection method is developed, which takes the gradient image of the left frame as a reference image to regulate the image feature selection. Experiments based on Middlebury benchmark have verified the superiority of the proposed algorithm.