Three-dimensional surface reconstruction via a robust binary shape-coded structured light method

Abstract. A binary shape-coded structured light method for single-shot three-dimensional reconstruction is presented. The projected structured pattern is composed with eight geometrical shapes with a coding window size of 2×2. The pattern element is designed as rhombic with embedded geometrical shapes. The pattern feature point is defined as the intersection of two adjacent rhombic shapes, and a multitemplate-based feature detector is presented for its robust detection and precise localization. Based on the extracted grid-points, a topological structure is constructed to separate the pattern elements from the obtained image. In the decoding stage, a training dataset is first established from training samples that are collected from a variety of target surfaces. Then, the deep neural network technique is applied for the classification of pattern elements. Finally, an error correction algorithm is introduced based on the epipolar and neighboring constraints to refine the decoding results. The experimental results show that the proposed method not only owns high measurement precision but also has strong robustness to surface color and texture.