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

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 (CNN...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-07, Vol.20 (13), p.3718
Main Authors: Nguyen, Hieu, Wang, Yuzeng, Wang, Zhaoyang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer architecture
Datasets
Deep learning
depth measurement
fringe projection
Light
Machine learning
Measurement techniques
Neural networks
Projectors
structured light
Three dimensional imaging
three-dimensional image acquisition
three-dimensional sensing
three-dimensional shape reconstruction
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Summary: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 as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20133718