Robust Single-Shot 3D Reconstruction by Sparse-to-Dense Stereo Matching and Spline Function Based Parallax Modeling

Single-shot 3D surface imaging techniques with high accuracy and high resolution are very important in both academia and industry. In this paper, we propose a sparse-to-dense structured light (SL) line-pattern based active stereo vision (ASV) approach to reconstruct the 3D shapes robustly with high-resolution. We propose a sparse-to-dense stereo matching (SDSM) method to solve the challenging problem of line clustering and line matching. We design the structured light line pattern with four colors and the distances between lines of different color range from sparse to dense. Accordingly, the sparse color lines could be clustered and matched at first while the dense color lines could be matched subsequently with the constraint of the clustered and matched sparse color lines. After all the color lines are matched, a spline-function based parallax model (SFPM) is computed based on the points on the matched color lines. Then, the depths of the points in the regions between the color lines are computed by the parallax model. Experimental results show that the proposed SDSM-SFPM ASV approach is more robust than existing methods especially in reconstructing the complex 3D shapes.