Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks.

ABSTRACT: Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well a

SUBMITTER: Nguyen H 

PROVIDER: S-EPMC7374384 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature