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Photonics
Special Issues
Optical 3D Sensing Technology and Application
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Optical 3D Sensing Technology and Application

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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 5043

Special Issue Editors

Dr. Lei Lü

E-Mail Website
Guest Editor
College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China
Interests: structured light technology; phase shifting profilometry; 3d reconstruction
Dr. Yongkai Yin

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong University, Jinan, China
Interests: Optical 3D imaging and measurement; machine vision;computational imaging

Special Issue Information

Dear Colleagues,

Three-dimensional information plays an essential role in many scenarios, such as industry, medicine, entertainment, etc. Optical 3D sensing is a key technology acquiring 3D information because of its advantages of non-destructiveness and high efficiency. With the development of the imaging principle, opto-electronic devices, computational hardware, artificial intelligence, etc., optical 3D sensing technology has been increasingly promoted. Numerous innovative technologies addressing the issues of high dynamic range, high speed, dynamic scene, etc. have been proposed. This Special Issue focuses on recent developments in optical 3D sensing technology and their applications in various scenarios.

The topics of interest include (but are not limited to) the following:

  • Optical 3D sensing;
  • Three-dimensional reconstruction;
  • Three-dimensional data processing;
  • Three-dimensional image acquisition and display;
  • Stereo vision;
  • Single-pixel imaging and sensing;
  • Deep-learning-based 3D sensing;
  • Three-dimensional sensing on robot;
  • Three-dimensional sensing on biomedicine;
  • Three-dimensional sensing on navigation;
  • Three-dimensional sensing on inspection;
  • Three-dimensional sensing on cultural heritage.

Dr. Lei Lü
Dr. Yongkai Yin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical 3D sensing
  • 3D reconstruction
  • 3D data processing

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Published Papers (2 papers)

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Research

14 pages, 1396 KiB  
Article
TPDNet: Texture-Guided Phase-to-DEPTH Networks to Repair Shadow-Induced Errors for Fringe Projection Profilometry
by Jiaqiong Li and Beiwen Li
Photonics 2023, 10(3), 246; https://doi.org/10.3390/photonics10030246 - 24 Feb 2023
Cited by 4 | Viewed by 1999
Abstract
This paper proposes a phase-to-depth deep learning model to repair shadow-induced errors for fringe projection profilometry (FPP). The model comprises two hourglass branches that extract information from texture images and phase maps and fuses the information from the two branches by concatenation and [...] Read more.
This paper proposes a phase-to-depth deep learning model to repair shadow-induced errors for fringe projection profilometry (FPP). The model comprises two hourglass branches that extract information from texture images and phase maps and fuses the information from the two branches by concatenation and weights. The input of the proposed model contains texture images, masks, and unwrapped phase maps, and the ground truth is the depth map from CAD models. A loss function was chosen to consider image details and structural similarity. The training data contain 1200 samples in the verified virtual FPP system. After training, we conduct experiments on the virtual and real-world scanning data, and the results support the model’s effectiveness. The mean absolute error and the root mean squared error are 1.0279 mm and 1.1898 mm on the validation dataset. In addition, we analyze the influence of ambient light intensity on the model’s performance. Low ambient light limits the model’s performance as the model cannot extract valid information from the completely dark shadow regions in texture images. The contribution of each branch network is also investigated. Features from the texture-dominant branch are leveraged as guidance to remedy shadow-induced errors. Information from the phase-dominant branch network makes accurate predictions for the whole object. Our model provides a good reference for repairing shadow-induced errors in the FPP system. Full article
(This article belongs to the Special Issue Optical 3D Sensing Technology and Application)
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18 pages, 6291 KiB  
Article
Wiggling-Related Error Correction Method for Indirect ToF Imaging Systems
by Zhaolin Zheng, Ping Song, Xuanquan Wang, Wuyang Zhang and Yunjian Bai
Photonics 2023, 10(2), 170; https://doi.org/10.3390/photonics10020170 - 5 Feb 2023
Cited by 2 | Viewed by 2442
Abstract
Indirect time-of-flight (ToF) imaging systems enable a broad array of applications owing to their high frame rate, strong durability, and low cost. However, the wiggling-related error caused by the harmonics in the emitted signal significantly affects the range accuracy of indirect ToF imaging [...] Read more.
Indirect time-of-flight (ToF) imaging systems enable a broad array of applications owing to their high frame rate, strong durability, and low cost. However, the wiggling-related error caused by the harmonics in the emitted signal significantly affects the range accuracy of indirect ToF imaging systems. In this paper, we establish a mathematical model of the wiggling-related error and propose a wiggling-related error correction method for indirect ToF imaging systems. This method adds a delay measurement and utilizes raw intensity measurements to evaluate the system state based on an adaptive Kalman filter (AKF), which is easy to implement in most indirect ToF imaging systems. Simulation and experimental results show that the proposed method performed well in reducing the wiggling-related error and had good robustness in different integration times. Compared with the existing methods, the proposed method not only has better performance but also is easier to implement. We believe that this study provides effective guidance for researchers understanding the wiggling-related error and a potential direction for the accuracy improvement of indirect ToF imaging systems. Full article
(This article belongs to the Special Issue Optical 3D Sensing Technology and Application)
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