An accurate measurement of high-reflective objects by using 3D structured light

Abstract

Using three-dimensional structured light to measure high-reflective objects accurately is a big challenge. Some overexposed and blurred areas in the structured light fringe patterns lead to the loss of semantic and texture information on the object surface, further leading to 3D model reconstruction errors. To solve this problem, a lightweight novel network, Deformable Convolutional and Multi-scale Convolutional Network (DcMcNet), is proposed for repairing highly reflectively distorted regions in sinusoidal fringe patterns and the Gray code binary fringe patterns. DcMcNet uses deformable convolution and multi-scale convolution to realize the effective utilization of multiscale global features in repairing highly reflective distorted regions and can adapt well to the different shapes of these regions. In addition, virtual software is used to acquire large-scale and high-fidelity datasets and a diverse loss function is constructed to better optimize DcMcNet. Depth reconstruction experiments of sinusoidal fringe patterns and point cloud reconstruction experiments of Gray code binary fringe patterns show that our method can repair highly reflective distortion regions with high accuracy, and for aeronautical blades, the MAE of the depth map is reduced from 0.1608 to 0.0675, and the point cloud coverage is improved from 76.64% to 98.95%.