High-speed 3D shape measurement with the multi-view system using deep learning

In this paper, we propose a high-speed three dimensional (3D) shape measurement with the multi-view system using deep learning. Common stereo matching methods are based on block-matching or graph cuts to build the global correspondence of stereo images and obtain the dense disparity map. For fringe projection profilometry (FPP), a large number of stereo matching algorithms have been proposed to enhance the accuracy and computational efficiency of stereo matching and acquire the disparity map with sub-pixel precision by using phase constraint, geometric constraint, and depth constraint. However, the universality and precision of these methods are still not enough which is difficult to meet high-precision and high-efficient 3D measurement applications. Inspired by deep learning techniques, we demonstrate that the deep neural networks can learn to perform stereo matching after appropriate training, which substantially improves the reliability and efficiency of stereo matching compared with the traditional approach. Besides, to acquire 3D results with high performance, the optimal design of the patterns projected by the projector is discussed in detail, and the relative spatial positions between the cameras and the projector are carefully adjusted in our multi-view system. Experimental results demonstrate the stereo matching method using deep learning provides better matching efficiency to realize the absolute 3D measurement for objects with complex surfaces.