On-line Dense 3D Reconstruction Method based on ANN us-ing a Single Structured Light Pattern

Abstract

Dense 3D reconstruction techniques to measuring dynamic scenes and deformable objects with little texture have been widely studied for various applications like surface deformation measurement. We assume that the actual situation of scanning is acquiring sequential shape and measuring surface deformation online at the moment when an object deforms under the action of aerodynamic forces. This paper presents such a technique for on-line acquisition of 3D surface data based on a one-shot scanning method that reconstructs 3D shape from a single image where a simple color-coded pattern using de Bruijn sequence is projected onto an object. The proposed approach has advantages that it requires no assumption of global smoothness and continuous surface. To realize 3D reconstruction from a single image, there are sever-al issues to be solved, for example, difficulty on decoding structured light pattern because of influence of chromatic aberration in both projection systems and imaging systems, and difficul-ty on establishing accurate correspondences between projector and camera pixels. This paper describes the solutions of the issues by combining two methods, that is (1) an efficient and robust pattern decoding method based on artificial neural networks (ANN), and (2) a sub-pixel matching method using phase map and multiple view geometry. Furthermore, we adopt some effective strategies to improve algorithm robustness. Practical experiments were carried out to test the accuracy and efficiency of the scanning system in typical configuration. The results show that the scanning system can reconstruct 3D shape on-line in high-resolution with the accuracy of 0.09 mm and efficiency of 10 fps.