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Special Issue "Sensing and Processing for 3D Computer Vision"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Prof. Dr. Denis Laurendeau
Website
Collection Editor
Computer Vision and Systems Laboratory, Laval University, 1665 Rue de l’Universite, Universite Laval, Quebec City, QC G1V 0A6, Canada
Interests: 3D sensors; active vision; 3D image processing and understanding; modelling; geomery; 3D sensing and modelling for augmented and virtual reality; applications of 3D computer vision

Special Issue Information

Dear Colleagues,

This Special Issue is targetting the submission of research articles on 3D sensing technology and the use of advanced 3D sensors in computer vision. Original contributions on novel active 3D sensors, stereo reconstruction approaches and sensor calibration techniques are solicited. Articles on 3D point cloud/mesh processing, geometric modelling, shape representation and recognition are also of interest for this Special Issue. Articles on the application of 3D sensing and modelling to metrology, industrial inspection and quality control, augmented/virtual reality, heritage preservation, arts and other fields are also welcome.

Prof. Dr. Denis Laurendeau
Collection Editor

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 papers will be 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. Sensors is an international peer-reviewed open access semimonthly 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 2000 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

  • Active/passive 3D sensors
  • Sensor calibration
  • Stereo reconstruction
  • Point cloud/mesh processing
  • Geometry
  • Modelling and representation
  • Shape analysis and recognition
  • Applications of 3D vision

Published Papers (3 papers)

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Open AccessLetter
High-Speed Measurement of Shape and Vibration: Whole-Field Systems for Motion Capture and Vibration Modal Analysis by OPPA Method
Sensors 2020, 20(15), 4263; https://doi.org/10.3390/s20154263 - 30 Jul 2020
Abstract
In shape measurement systems using a grating projection method, the phase analysis of a projected grating provides accurate results. The most popular phase analysis method is the phase shifting method, which requires several images for one shape analysis. Therefore, the object must not [...] Read more.
In shape measurement systems using a grating projection method, the phase analysis of a projected grating provides accurate results. The most popular phase analysis method is the phase shifting method, which requires several images for one shape analysis. Therefore, the object must not move during the measurement. The authors previously proposed a new accurate and high-speed shape measurement method, i.e., the one-pitch phase analysis (OPPA) method, which can determine the phase at every point of a single image of an object with a grating projected onto it. In the OPPA optical system, regardless of the distance of the object from the camera, the one-pitch length (number of pixels) on the imaging surface of the camera sensor is always constant. Therefore, brightness data for one pitch at any point of the image can be easily analyzed to determine phase distribution, or shape. This technology will apply to the measurement of objects in motion, including automobiles, robot arms, products on a conveyor belt, and vibrating objects. This paper describes the principle of the OPPA method and example applications for real-time human motion capture and modal analysis of free vibration of a flat cantilever plate after hammering. The results show the usefulness of the OPPA method. Full article
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision)
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Open AccessArticle
Quantitative 3D Reconstruction from Scanning Electron Microscope Images Based on Affine Camera Models
Sensors 2020, 20(12), 3598; https://doi.org/10.3390/s20123598 - 26 Jun 2020
Abstract
Scanning electron microscopes (SEMs) are versatile imaging devices for the micro- and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed [...] Read more.
Scanning electron microscopes (SEMs) are versatile imaging devices for the micro- and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed morphological characterizations require knowledge about the three-dimensional (3D) surface structure. To overcome this limitation, a reconstruction routine is presented that allows the quantitative depth reconstruction from SEM image sequences. Based on the SEM’s imaging properties that can be well described by an affine camera, the proposed algorithms rely on the use of affine epipolar geometry, self-calibration via factorization and triangulation from dense correspondences. To yield the highest robustness and accuracy, different sub-models of the affine camera are applied to the SEM images and the obtained results are directly compared to confocal laser scanning microscope (CLSM) measurements to identify the ideal parametrization and underlying algorithms. To solve the rectification problem for stereo-pair images of an affine camera so that dense matching algorithms can be applied, existing approaches are adapted and extended to further enhance the yielded results. The evaluations of this study allow to specify the applicability of the affine camera models to SEM images and what accuracies can be expected for reconstruction routines based on self-calibration and dense matching algorithms. Full article
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision)
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Open AccessArticle
3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging
Sensors 2020, 20(15), 4211; https://doi.org/10.3390/s20154211 - 29 Jul 2020
Abstract
Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape [...] Read more.
Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape of transparent objects, thereby avoiding the rotation or movement of the object during imaging. In addition, a simple and cost-effective experimental setup is presented, which employs two single-pixel detectors and a digital micromirror device, for imaging transparent objects by projecting binary patterns. In the system setup, a dark framework is implemented around the object, to create shades at the boundaries of the object. By triangulating the light path from the object, the surface shape is recovered, neither considering the reflections nor the number of refractions. It can, therefore, handle transparent objects with a relatively complex shape with the unknown refractive index. The implementation of compressive sensing in this technique further simplifies the acquisition process, by reducing the number of measurements. The experimental results show that 2D images obtained from the single-pixel detectors are better in quality with a resolution of 32×32. Additionally, the obtained disparity and error map indicate the feasibility and accuracy of the proposed method. This work provides a new insight into 3D transparent object detection and reconstruction, based on single-pixel imaging at an affordable cost, with the implementation of a few numbers of detectors. Full article
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision)
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