Deep-learning-assisted single-shot 3D shape and color measurement using color fringe projection profilometry

Abstract

The demand for fast, accurate, and cost-effective methods for three-dimensional shape and color measurements has been increasing. Ideally, both the shape and color of an object should be obtained in a single shot. Color fringe projection profilometry allows single-shot 3D shape measurement; however, it faces challenges when applied to colored objects. The fringe patterns are attenuated, leading to inaccuracies in shape measurement, and the fringes obscure the object's color information. This study proposes a novel approach to address these challenges by using a deep learning-based ResUNet model. Our method uses two independently trained ResUNets to correct fringe distortions for improved shape measurement accuracy and to remove fringe patterns for color information extraction from the same captured images. The simulation and experimental results demonstrate the effectiveness and applicability of this approach for single-shot 3D shape and color measurements.