PATCH-BASED STEREO MATCHING USING 3D CONVOLUTIONAL NEURAL NETWORKS

In this paper, we propose patch-based stereo matching using 3D convolutional neural networks (CNN). We extract spatial color and disparity features simultaneously through 3D CNN. We treat stereo matching as multi-class classification that the classes are all possible disparity values. We first generate a large set of patches from stereo images for 3D CNN. Then, we get an initial disparity map through 3D CNN and refine it using color image guided filtering. The color image guided filtering minimizes outliers and refines edges in disparity without texture copying artifacts. Experimental results show that the proposed method successfully estimates disparity in smooth and discontinuity regions while preserving edges as well as outperforms state-of-the-arts in terms of average errors.