Optical Imaging for 3D Surface and Phase Recovery: Techniques and Applications

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: 20 August 2026 | Viewed by 895

Editors


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Guest Editor
State Key Lab of Heterogeneous Integration, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: computational sensing and imaging; signal and image processing; machine learning; instrumentation; precision manufacturing; applied robotics

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Guest Editor
Department of Optoelectronic Information Science and Technology, Jiangnan University, Wuxi 214122, China
Interests: optical imaging; metrology; ptychography

Special Issue Information

Dear Colleagues,

The Special Issue, “Optical Imaging for 3D Surface and Phase Recovery: Techniques and Applications”, aims to gather recent advances, novel methodologies, and transformative applications in the field of computational optical imaging. It focuses on recovering 3D structures and quantitative phase maps. The Special Issue seeks to bridge theory and experimental investigation by highlighting emerging trends across biomedical imaging, industrial metrology, and optical sensing, where precise phase retrieval and 3D reconstruction have become pivotal to scientific discovery and engineering innovation.

Topics of interest include, but are not limited to, the following: quantitative phase imaging, digital holography, interferometric microscopy, coherent diffraction imaging, optical tomography, light-field, ptychography, structured illumination and fringe projection profilometry, time-of-flight, stereo, wavefront sensing, and AI-enhanced imaging and phase recovery. The Special Issue welcomes both fundamental studies—advancing optical physics and computational reconstruction—and applied research demonstrating practical utility in biomedicine, materials science, or precision manufacturing.

Optical imaging has evolved into a cornerstone of modern science and technology, offering unparalleled capabilities for visualizing structure, dynamics, and function across scales. Among its diverse modalities, 3D and phase-resolved imaging techniques occupy a central role as they enable the reconstruction of both the geometric and optical properties of samples. Recent years have witnessed a rapid convergence between optical hardware innovation and computational reconstruction algorithms, driven by advances in inverse imaging theory, deep learning, and high-performance computation. This synergy has enabled 3D and quantitative phase imaging in  biology, industrial metrology, and real-time inspection in semiconductor and photonic manufacturing. At the same time, multimodal and multi-wavelength systems have broadened the application landscape, providing richer structural and functional information for complex samples.

The objective of this Special Issue is to present a comprehensive overview of the latest progress in this rapidly expanding field. By uniting contributions from physics, engineering, computational imaging, and artificial intelligence communities, we aim to promote cross-disciplinary dialogue, identify emerging challenges, and inspire next-generation solutions that will push the boundaries of precision optical measurement, biomedical diagnostics, and intelligent imaging systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: quantitative phase imaging, digital holography, interferometric microscopy, coherent diffraction imaging, optical tomography, light-field imaging, ptychography, structured illumination and fringe projection profilometry, time-of-flight, stereo, wavefront sensing, and AI-enhanced imaging and phase recovery.

We look forward to receiving your contributions.

Prof. Dr. Yibin Tian
Prof. Dr. Cheng Liu
Guest Editors

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Keywords

  • optical imaging
  • 3D reconstruction
  • phase recovery
  • deep learning

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Published Papers (1 paper)

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Research

26 pages, 8518 KB  
Article
CVA-Net: Multi-View 3D Reconstruction for Fringe Projection Profilometry via Cross-View Attention and Sim2Real Learning
by Zuqiong Chen, Xiaopin Zhong and Yibin Tian
Photonics 2026, 13(6), 601; https://doi.org/10.3390/photonics13060601 - 21 Jun 2026
Viewed by 315
Abstract
Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that [...] Read more.
Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm2 and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article
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