An Efficient High Dynamic Range Structured Light 3-D Measurement System Based on Adaptive Brightness Control Network

Point clouds of reflective objects are often incomplete in fringe projection structured light 3-D measurement. Traditional solutions are always computationally inefficient, whereas the speedy forward propagation of neural networks can overcome this drawback. So, in this article, we propose a novel deep learning method capable of adaptively controlling the projection brightness in specific regions with a high dynamic range for complete point cloud acquisition. First, we describe the structured light system (SLS) and the physical model used. Second, we construct a convolutional neural network (CNN) incorporating physical prior knowledge, which enables full-process high-resolution multichannel image feature extraction and directly outputs pixel-level optimal projection brightness. Meanwhile, we propose a simulated dataset generation method based on stochastic reflectivity together with a network training approach to solve network training difficulties. Further, the robust fringe encoding and decoding methods are presented. Sufficient comparative experiments show that our CNN-based SLS can achieve more accurate brightness adjustment with fewer projections, higher computational efficiency, and applicability.