Adaptive Smoothness Constraints For Efficient Stereo Matching Using Texture And Edge Information

An efficient stereo matching algorithm, which applies adaptive smoothness constraints using texture and edge information, is proposed in this work. First, we determine non-textured regions, on which an input image yields flat pixel values. In the non-textured regions, we penalize depth discontinuity and complement the primary CNN-based matching cost with a color-based cost. Second, by combining two edge maps from the input image and a pre-estimated disparity map, we extract denoised edges that correspond to depth discontinuity with high probabilities. Thus, near the denoised edges, we penalize small differences of neighboring disparities. Based on these adaptive smoothness constraints, the proposed algorithm outperforms the conventional methods significantly and achieves the state-of-the-art performance on the Middlebury stereo benchmark.