Super-Resolution Phase Retrieval Network for Single-Pattern Structured Light 3D Imaging

Abstract

Structured light 3D imaging is often used for obtaining accurate 3D information via phase retrieval. Single-pattern structured light 3D imaging is much faster than multi-pattern versions. Current phase retrieval methods for single-pattern structured light 3D imaging are however not accurate enough. Besides, the projector resolution in a structured light 3D imaging system is expensive to improve due to hardware costs. To address the issues of low accuracy and low resolution of single-pattern structured light 3D imaging, this work proposes a super-resolution phase retrieval network (SRPRNet). Specifically, a phase-shifting module is proposed to extract multi-scale features with different phase shifts, and a refinement and super-resolution module is proposed to obtain refined and super-resolution phase components. After phase demodulation and unwrapping, high-resolution absolute phase is obtained. A sine shifting loss and a cosine shifting loss are also introduced to form the regularization term of the loss function. As far as can be ascertained, the proposed SRPRNet is the first network for super-resolution phase retrieval by using a single pattern, and it can also be used for standard-resolution phase retrieval. Experimental results on three datasets show that SRPRNet achieves state-of-the-art performance on 1×, 2×, and 4× superresolution phase retrieval tasks.

Single-Shot 3D Reconstruction via Nonlinear Fringe Transformation: Supervised and Unsupervised Learning Approaches

“…Subsequently, the wrapped phase map can serve as either the input or output vector in co-operation between fringe projection and learning-based networks [55][56][57][58]. Recognizing the significance of the integer fringe orders in the phase unwrapping scheme, several methods have trained CNN models to segment the integer fringe orders or predict the coarse phase map [59][60][61][62]. In recent research, instead of employing multiple networks or a multi-stage scheme to determine separate wrapped phases, fringe orders, or coarse phase maps, a single network with multiple decoder branches has been developed to predict multiple intermediate quantities for determining the unwrapped phase map [63][64][65].…”

Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning

“…It involves transforming the fringe pattern(s) into intermediate results, which ultimately enable the acquisition of precise phase distributions. These phase distributions and camera calibration information are then utilized to achieve accurate 3D reconstruction [ 49 , 50 , 51 , 52 , 53 , 54 , 55 ].…”