ArticleSensors (Basel, Switzerland)2020
Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Vision-Based Artificial Intelligence for Adaptive Peen Forming: Sensing Architectures, Learning Models, and Closed-Loop Smart Manufacturing.Sensors (Basel, Switzerland) · 2026Review
- Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration.Sensors (Basel, Switzerland) · 2025Review
- Deep-learning-based endoscopic single-shot fringe projection profilometry.Journal of biomedical optics · 2025Article
- Single-Model Self-Recovering Fringe Projection Profilometry Absolute Phase Recovery Method Based on Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis.Journal of imaging · 2024Article
- TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.Sensors (Basel, Switzerland) · 2024Article
- Single-Shot 3D Reconstruction via Nonlinear Fringe Transformation: Supervised and Unsupervised Learning Approaches.Sensors (Basel, Switzerland) · 2024Article
- Weakly Supervised Depth Estimation for 3D Imaging with Single Camera Fringe Projection Profilometry.Sensors (Basel, Switzerland) · 2024Article
- Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Single-Shot Multi-Frequency 3D Shape Measurement for Discontinuous Surface Object Based on Deep Learning.Micromachines · 2023Article
- Deep learning in optical metrology: a review.Light, science & applications · 2022Review
- Computer-aided diagnosis of prostate cancer based on deep neural networks from multi-parametric magnetic resonance imaging.Frontiers in physiology · 2022Article
- Three-Dimensional Morphology and Size Measurement of High-Temperature Metal Components Based on Machine Vision Technology: A Review.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.