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Short-Range Optical 3D Scanning and 3D Data Processing

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

Deadline for manuscript submissions: 1 September 2024 | Viewed by 15006

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Special Issue Editors

Dr. Farbod Khameneifar

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Guest Editor

Department of Mechanical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada
Interests: 3D scanning; 3D metrology; computer-aided inspection

Dr. Samanta Piano

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Guest Editor

Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, Nottingham NG8 1BB, UK
Interests: optical form metrology; machine learning for metrology applications
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Dear Colleagues,

Optical 3D scanning is becoming increasingly popular for collecting high-density 3D point cloud data from an object in a variety of applications, including manufacturing metrology. The industry needs hardware and software tools for high-speed high-accuracy 3D data acquisition and processing, as well as methods for the evaluation of uncertainty of scan data.

This special issue aims to present recent research on short-range optical 3D scanning and 3D data processing. We welcome the research works (in the form of research articles or reviews) on sensor development and calibration, multi-sensor data fusion, metrological performance evaluation and uncertainty estimation, and digital geometry processing tasks, including point cloud de-noising and smoothing, registration, segmentation, and geometric reconstruction. Manuscripts on defect detection and in-process metrology of manufactured parts are also welcome.

Dr. Farbod Khameneifar
Dr. Samanta Piano
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Vesna Marinkovic via <vesna.marinkovic@mdpi.com> directly.

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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 2600 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

3D scanning
3D metrology
optical metrology
in-process metrology
computer-aided inspection
reverse engineering
point cloud processing
mesh processing

Published Papers (7 papers)

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Research

29 pages, 4039 KiB

Open AccessArticle

Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera

by Tim Michels, Daniel Mäckelmann and Reinhard Koch

Sensors 2024, 24(8), 2522; https://doi.org/10.3390/s24082522 - 15 Apr 2024

Viewed by 349

Abstract

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera’s main lens and microlenses. Our work addresses the often-overlooked role of the main lens exit pupil in these models, specifically in the decoding process of standard plenoptic camera (SPC) images. We formally deduce the connection between the refocusing distance and the resampling parameter for the decoded light field and provide an analysis of the errors that arise when the exit pupil is not considered. In addition, previous work is revisited with respect to the exit pupil’s role, and all theoretical results are validated through a ray tracing-based simulation. With the public release of the evaluated SPC designs alongside our simulation and experimental data, we aim to contribute to a more accurate and nuanced understanding of plenoptic camera optics. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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19 pages, 5687 KiB

Open AccessArticle

Determination of the Accuracy of the Straight Bevel Gear Profiles by a Novel Optical Coaxial Multi-Ring Measurement Method

by Junheng Li, Dehai Zhang, Yanqin Li, Xuanxiong Ma, Tao Wang and Chao Wu

Sensors 2023, 23(5), 2654; https://doi.org/10.3390/s23052654 - 28 Feb 2023

Cited by 1 | Viewed by 1667

Abstract

Straight bevel gears are widely used in mining equipment, ships, heavy industrial equipment, and other fields due to their high capacity and robust transmission. Accurate measurements are essential in order to determine the quality of bevel gears. We propose a method for measuring the accuracy of the top surface profile of the straight bevel gear teeth based on binocular visual technology, computer graphics, error theory, and statistical calculations. In our method, multiple measurement circles are established at equal intervals from the small end of the top surface of the gear tooth to the large end, and the coordinates of the intersection points of these circles with the tooth top edge lines of the gear teeth are extracted. The coordinates of these intersections are fitted to the top surface of the tooth based on NURBS surface theory. The surface profile error between the fitted top surface of the tooth and the designed surface is measured and determined based on the product use requirements, and if this is less than a given threshold, the product is acceptable. With a module of 5 and an eight-level precision, such as the straight bevel gear, the minimum surface profile error measured was −0.0026 mm. These results demonstrate that our method can be used to measure surface profile errors in the straight bevel gears, which will broaden the field of in-depth measurements for the straight bevel gears. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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15 pages, 5927 KiB

Open AccessArticle

Robust Mesh Segmentation Using Feature-Aware Region Fusion

by Lulu Wu, Yu Hou, Junli Xu and Yong Zhao

Sensors 2023, 23(1), 416; https://doi.org/10.3390/s23010416 - 30 Dec 2022

Cited by 2 | Viewed by 2547

Abstract

This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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17 pages, 4132 KiB

Open AccessArticle

Feature Consistent Point Cloud Registration in Building Information Modeling

by Hengyu Jiang, Pongsak Lasang, Georges Nader, Zheng Wu and Takrit Tanasnitikul

Sensors 2022, 22(24), 9694; https://doi.org/10.3390/s22249694 - 10 Dec 2022

Cited by 1 | Viewed by 1413

Abstract

Point Cloud Registration contributes a lot to measuring, monitoring, and simulating in building information modeling (BIM). In BIM applications, the robustness and generalization of point cloud features are particularly important due to the huge differences in sampling environments. We notice two possible factors that may lead to poor generalization, the normal ambiguity of boundaries on hard edges leading to less accuracy in transformation; and the fact that existing methods focus on spatial transformation accuracy, leaving the advantages of feature matching unaddressed. In this work, we propose a boundary-encouraging local frame reference, the

P y r a m i d F e a t u r e (P M D)

, consisting of point-level, line-level, and mesh-level information to extract a more generalizing and continuous point cloud feature to encourage the knowledge of boundaries to overcome the normal ambiguity. Furthermore, instead of registration guided by spatial transformation accuracy alone, we suggest another supervision to extract consistent hybrid features. A large number of experiments have demonstrated the superiority of our PyramidNet (PMDNet), especially when the training (ModelNet40) and testing (BIM) sets are very different, PMDNet still achieves very high scalability. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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23 pages, 12586 KiB

Open AccessArticle

Quality Assessment of a Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces—Accuracy Evaluation and Validation

by Barış Özcan and Jörg Blankenbach

Sensors 2022, 22(11), 4211; https://doi.org/10.3390/s22114211 - 31 May 2022

Cited by 2 | Viewed by 1682

Abstract

The roughness of a surface is a decisive parameter of a material. In rehabilitation of concrete structures, for example, it significantly affects the adhesion between the coating material and the base concrete. However, the standard measurement procedure in construction suffers from considerable disadvantages, which leads to the demand for more sophisticated methods. In a research project, we, therefore, developed a novel camera-based measurement system, which is customized to meet the prevailing requirements for practical use on construction sites. In this article, we provide an overview of the measurement system and present comprehensive examinations to evaluate the accuracy and to provide evidence of validity. First, we examined the accuracy of the system by empirically assessing both trueness and precision of measurements using three concrete specimens. Trueness was determined by comparing the surface measurements to those of a highly accurate microscope system, revealing RMSE values of around 40–50 µm. Precision, on the other hand, was assessed considering the scattering of the roughness measurements under repeat conditions, which led to standard deviations of less than 6 µm. Furthermore, to proof validity, a comparative study was conducted based on sixteen concrete specimens, which includes the sand patch method and laser triangulation as established roughness measurement methods in practice. The empirically determined correlation coefficients between all three methods were greater than 0.99, indicating extraordinarily high linear relationships. Among them, the greatest correlation was between the camera-based system and laser triangulation. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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18 pages, 11288 KiB

Open AccessArticle

Ground Control System for UAS Safe Landing Area Determination (SLAD) in Urban Air Mobility Operations

by Gennaro Ariante, Salvatore Ponte, Umberto Papa, Alberto Greco and Giuseppe Del Core

Sensors 2022, 22(9), 3226; https://doi.org/10.3390/s22093226 - 22 Apr 2022

Cited by 4 | Viewed by 2731

Abstract

The use of the Unmanned Aerial Vehicles (UAV) and Unmanned Aircraft System (UAS) for civil, scientific, and military operations, is constantly increasing, particularly in environments very dangerous or impossible for human actions. Many tasks are currently carried out in metropolitan areas, such as urban traffic monitoring, pollution and land monitoring, security surveillance, delivery of small packages, etc. Estimation of features around the flight path and surveillance of crowded areas, where there is a high number of vehicles and/or obstacles, are of extreme importance for typical UAS missions. Ensuring safety and efficiency during air traffic operations in a metropolitan area is one of the conditions for Urban Air Mobility (UAM) operations. This paper focuses on the development of a ground control system capable of monitoring crowded areas or impervious sites, identifying the UAV position and a safety area for vertical landing or take-off maneuvers (VTOL), ensuring a high level of accuracy and robustness, even without using GNSS-derived navigation information, and with on-board terrain hazard detection and avoidance (DAA) capabilities, in particular during operations conducted in BVLOS (Beyond Visual Line Of Sight). The system is composed by a mechanically rotating real-time LiDAR (Light Detection and Ranging) sensor, linked to a Raspberry Pi 3 as SBC (Session Board Controller), and interfaced to a GCS (Ground Control Station) by wireless connection for data management and 3-D information transfer. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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20 pages, 48219 KiB

Open AccessArticle

Digital Twin of an Optical Measurement System

by Michiel Vlaeyen, Han Haitjema and Wim Dewulf

Sensors 2021, 21(19), 6638; https://doi.org/10.3390/s21196638 - 06 Oct 2021

Cited by 15 | Viewed by 2952

Abstract

Digital twins of measurement systems are used to estimate their measurement uncertainty. In the past, virtual coordinate measuring machines have been extensively researched. Research on digital twins of optical systems is still lacking due to the high number of error contributors. A method to describe a digital twin of an optical measurement system is presented in this article. The discussed optical system is a laser line scanner mounted on a coordinate measuring machine. Each component of the measurement system is mathematically described. The coordinate measuring machine focuses on the hardware errors and the laser line scanner determines the measurement error based on the scan depth, in-plane angle and out-of-plane angle. The digital twin assumes stable measurement conditions and uniform surface characteristics. Based on the Monte Carlo principle, virtual measurements can be used to determine the measurement uncertainty. This is demonstrated by validating the digital twin on a set of calibrated ring gauges. Two validation tests are performed: the first verifies the virtual uncertainty estimation by comparison with experimental data. The second validates the measured diameter of different ring gauges by comparing the estimated confidence interval with the calibrated diameter. Full article

(This article belongs to the Special Issue Short-Range Optical 3D Scanning and 3D Data Processing)

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