HNet: Single-shot 3D shape reconstruction using structured light and h-shaped global guidance network

Abstract

Retrieving three-dimensional (3D) shape information from a single two-dimensional (2D) image has recently gained enormous attention in a variety of fields. In spite of recent advancements in algorithms and hardware developments, the easy-to-use characteristics and the accuracy of the 3D shape reconstruction are always of great interest. This paper presents a robust 3D shape reconstruction technique that integrates structured-light 3D imaging scheme with deep convolutional neural network (CNN) learning. The structured-light patterns facilitate the featuring process while the CNN modeling surpasses the complexity of the traditional 3D shape reconstructions. In the supervised learning pipeline, the input is either a single fringe-pattern or a single speckle-pattern image, and the output is its corresponding high-accuracy 3D shape label. Unlike the well-received autoencoder-based CNN model, a global guidance network path with multi-scale feature fusion is introduced into the CNN model to improve the accuracy of the 3D shape reconstruction. Experimental evaluations have been conducted to demonstrate the validity and robustness of the proposed technique, which provides a promising tool for ever-increasing scientific research and engineering applications.