Stereo Rectification Based on Epipolar Constrained Neural Network

Abstract

This paper proposes a novel deep neural network-based method for stereo image rectification. The neural network is mainly based on the theoretical basis of epipolar constraints from multi-view geometry and intensity constraints of images, which separately describes the relationship of the corresponding epipolar lines between a pair of image, including the epipolar-line slope and y-intercept consistency of the epipolar lines and the consistency of the corresponding intensity values between two images. Benefiting from the designed rectification framework together with a feature matching module to extract accurate corresponding key-points between views, our method is able to realize a stable and accurate stereo rectification process. Compared with classic feature-based rectification methods, our proposed method can rectify small errors, and achieve a much more accurate rectification performance. Experiments conducted on synthetic face dataset and real-world KITTI dataset demonstrate the effectiveness and robustness of the proposed method.