3D colour object reconstruction based on Growing Neural Gas

Abstract

With the advent of low-cost 3D sensors and 3D printers, surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost sensors. We have modified the Growing Neural Gas (GNG) network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects. Some improvements have been made on the original algorithm considering colour information during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to create 3D faces online, whereas existing 3D reconstruction methods based on Self-Organizing Maps (SOMs) required post-processing steps to close gaps and holes produced during the 3D reconstruction process. Performed experiments validated how the proposed method improves existing techniques removing post-processing steps and including colour information in the final triangular mesh.