Depth acquisition with the combination of structured light and deep learning stereo matching

Abstract The methods of structured light and deep learning are widely used in artificial vision to acquire a depth map of real-world scenes. In this paper, we propose a novel method of combining structured light and deep learning stereo matching to calculate the depth. To combat the problems with textureless areas of stereo matching, a pair of left and right side images with abundant structured light information is adopted to acquire a coarse depth map by unsupervised CNN networks. The coarse depth map is used for phase unwrapping, which is a bottleneck in the phase-based structured light method. Then, a fine and accurate depth map is obtained by phase matching. Compared with the traditional structured light method, the proposed method performs better on occluded and textureless areas. To evaluate the performance of our proposed method, an experimental platform is established and several experiments are conducted. Quantitative and qualitative experiments demonstrate that the proposed method can generate a high precision depth and relieve the occlusion in the structured light system.