Deep Illumination Invariance Feature Enhancement and Trilateral Feature Aggregation for Stereo Matching Network

Stereo matching is an important part of robot vision intelligent environment perception. Currently, reflective regions and large textureless regions, repetitive texture regions, and depth discontinuity regions are still difficult and error-prone for stereo matching tasks. We propose a novel stereo matching network that can effectively solve the above problems. First, to address the difficulty in reflective regions and large textureless regions, we propose the illumination invariance feature enhancing module which can promote the feature extraction network to extract more sufficient features in reflective regions and large textureless regions. The illumination invariance feature enhancing module is a differentiable transformation of the Census-based matching cost computation and can be combined with multiple stereo networks to improve their performance in difficult regions. Then to address the difficulty in repetitive texture regions and depth discontinuity regions, we propose the differentiable trilateral feature aggregation module to make full use of the prior geometric knowledge of color similarity, spatial distance, and edge strength in the scene to enhance the aggregation ability of cost agregation network in depth discontinuity regions and repetitive texture regions. The proposed network is end-to-end and is verified by extensive experiments. The results show that the proposed method can effectively solve the problems of stereo matching in the above difficult regions and has achieved the competitive performance on Scene Flow datasets, KITTI 2012 datasets, and KITTI 2015 datasets.