A Structured Light System Encoding for an Uncalibrated 3D Reconstruction Based on Evolutionary Algorithms

This paper deals with 3D shape reconstruction based on a structured-light projector and two cameras using Evolutionary Algorithms (EAs). Two main problems are solved: first, the correspondence problem (also called the stereo matching problem) which allows the matching of different views of a same point in the two images, and second, the camera calibration which takes into account the camera parameters and the camera relative locations through a fundamental matrix. Concerning the correspondence problem, the main issue is the detection of the points to be matched, i.e. the points of interest (POIs). In this paper we propose a structured pattern to be projected on the object to analyse. The structured pattern is composed of triangles with different colors and different orientations. The POIs to be matched are the vertices of the triangles. Then we define an encoding method allowing the recovery of all the POIs that are detected in both images. In previous work, we showed that EAs can be used to obtain the fundamental matrix, thus providing an uncalibrated 3D reconstruction approach. However the method processed too small a number of POIs. The proposed acquisition system allows a high number of POIs to be obtained. The initial EAs are improved to use the new set of POIs. The experiments show that the proposed encoding method allows an efficient matching. In addition, accurate 3D reconstruction results are obtained and the process converges correctly.