3D Surface Reconstruction Using A Two-Step Stereo Matching Method Assisted with Five Projected Patterns

Three-dimensional vision plays an important role in robotics. In this paper, we present a 3D surface reconstruction scheme based on combination of stereo matching and pattern projection. A two-step matching scheme is proposed to establish reliable correspondence between stereo images with high computation efficiency and accuracy. The first step (coarse matching) can quickly find the correlation candidates, and the second step (precise matching) is responsible for determining the most precise correspondence within the candidates. Two phase maps serve as codewords and are utilized in the two-step stereo matching, respectively. The phase maps are derived from phase-shifting patterns to provide robustness to the background noises. Only five patterns are required, which reduces the image acquisition time. Moreover, the precision is further enhanced by applying a correspondence refinement algorithm. The precision and accuracy are validated by experiments on standard objects. Furthermore, various experiments are conducted to verify the capability of the proposed method, which includes the complex object reconstruction, the high-resolution reconstruction, and the occlusion avoidance. The real-time experimental results are also provided.