ArticleSensors (Basel, Switzerland)2023
Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration.Sensors (Basel, Switzerland) · 2025Review
- 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
- Single-Shot 3D Reconstruction via Nonlinear Fringe Transformation: Supervised and Unsupervised Learning Approaches.Sensors (Basel, Switzerland) · 2024Article
- Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional (3D) shape acquisition of objects from a single-shot image has been highly demanded by numerous applications in many fields, such as medical imaging, robotic navigation, virtual reality, and product in-line inspection. This paper presents a robust 3D shape reconstruction approach integrating a structured-light technique with a deep learning-based artificial neural network. The proposed approach employs a single-input dual-output network capable of transforming a single structured-light image into two intermediate outputs of multiple phase-shifted fringe patterns and a coarse phase map, through which the unwrapped true phase distributions containing the depth information of the imaging target can be accurately determined for subsequent 3D reconstruction process. A conventional fringe projection technique is employed to prepare the ground-truth training labels, and part of its classic algorithm is adopted to preserve the accuracy of the 3D reconstruction. Numerous experiments have been conducted to assess the proposed technique, and its robustness makes it a promising and much-needed tool for scientific research and engineering applications.
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Registered trials
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