Single-shot 3D shape reconstruction for complex surface objects with colour texture based on deep learning

Abstract

Three-dimensional (3D) shape reconstruction based on structured light technique is one of the most crucial techniques in the field of optical measurement due to the nature of non-contact and high-precision. In recent years, researchers found that the deep-learning method can significantly improve the quality and efficiency of 3D reconstruction. So far, the research object of structured light 3D reconstruction technology related to deep-learning methods usually adopts plaster models. However, the surface of the reconstructed objects always contains rich colour texture information in a real-world scenario, which affects the accuracy of the reconstruction, especially for some single-shot applications. To solve the above problem, we propose a deep-learning-based method single-shot 3D reconstruction method (SSR) for colour textured objects, and it can reconstruct colour textured objects with high precision. The experimental results show that the proposed method provides good guidance for the practical application and scientific research of fringe projection profilometry based on colour textured objects.