Deep Learning-Based Invalid Point Removal Method for Fringe Projection Profilometry

Abstract Fringe projection profilometry (FPP) has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed. The point cloud, which is a measurement result of the FPP system, typically contains a large number of invalid points caused by the background, ambient light, shadows, and object edge regions. Research on noisy point detection and elimination has been conducted over the past two decades. However, existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark. In this paper, we propose a novel invalid point removal framework that consists of two aspects: (1) A convolutional neural network (CNN) is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions. (2) A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects. Experimental results verify that the proposed framework (1) can remove background points intelligently and accurately in different types of complex circumstances, and (2) performs excellently in discrete point detection from object regions.