Sub-Pixel Depth Perception Based on Flexible and Separated Structured Light

We develop the method of machine learning for sub-pixel depth perception based on flexible and separated structured light, and describe novel framework for getting the extrinsic parameters of the system without complicated calibration in advance. We combined the extrinsic parameter calculation process into the structured light decoding process. PSO-C4.5 Decesion Tree algorithm is used to classify and match sub-pixel based on the Label. According to the matching result, the disparity map at the sub-pixel level is obtained, and the extrinsic parameters of the measurement system are obtained according to the fundamental matrix and initial pose information. In contrast to previous work, we formulate the problem of correspondence matching as a classification problem, which allows us to correctly take into account effects caused by scene movement. All optimization problems are solved with machine learning techniques. We test our method on a number of real-world examples as well as our new benchmark dataset for depth map estimation, and compare results to the state-of-the-art methods. The proposed method is both faster as well as more accurate.