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Symmetry
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Symmetry/Asymmetry in Artificial Intelligence for Point Cloud Data Processing
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Symmetry/Asymmetry in Artificial Intelligence for Point Cloud Data Processing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 1 November 2025 | Viewed by 1881

Special Issue Editors

Dr. Wuyong Tao

E-Mail Website
Guest Editor
School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
Interests: point cloud registration; object recognition; point cloud segmentation; classification
Dr. Xijiang Chen

E-Mail Website
Guest Editor
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430079, China
Interests: indoor point cloud; point cloud feature extraction
Dr. Yufu Zang

E-Mail Website
Guest Editor
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: laser scanning measurement; UAV photogrammetry

Special Issue Information

Dear Colleagues,

With the development of 3D acquisition techniques, e.g., Kinect and LiDAR, point cloud data are becoming more and more popular and are being increasingly used in many applications, such as 3D reconstruction, simultaneous localization and mapping (SLAM), building information management (BIM), automatic drive, virtual reality (VR), and augmented reality (AR). In order to better serve these applications, symmetry and asymmetry intelligent point cloud data processing plays a crucial role. Owing to different kinds of nuisances (including noise, density variation, occlusion, clutter, data missing, etc.) in the point clouds and complex scanning environment, the point cloud data processing faces great challenge. Therefore, point cloud data processing should be paid much attention, which is the topic of this Special Issue.We are pleased to invite you to submit your works about intelligent point cloud data processing and symmetry and asymmetry to this Special Issue. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: (1) Point cloud registration; (2) Symmetry/Asymmetry and Point cloud segmentation; (3) 3D object recognition; (4) Road object extraction and classification; (5) Building contour extraction; (6) Registration of point cloud and image; (7) Place recognition based on point cloud. We look forward to receiving your contributions.

Dr. Wuyong Tao
Dr. Xijiang Chen
Dr. Yufu Zang
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. Symmetry 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

  • 3D object recognition
  • point cloud registration
  • virtual reality

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Published Papers (2 papers)

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Research

19 pages, 2038 KB  
Article
Two-Dimensional Skeleton Intersection Extraction-Based Method for Detecting Welded Joints on the Three-Dimensional Point Cloud of Sieve Nets
by Haiping Zhong, Weigang Jian, Yuchen Yang, Wei Li and Liyuan Zhang
Symmetry 2025, 17(9), 1484; https://doi.org/10.3390/sym17091484 - 8 Sep 2025
Viewed by 441
Abstract
The concept of symmetry is a fundamental principle in various scientific and engineering fields, including welding technology. In the context of this paper, symmetry could play a role in optimizing the welding trajectory. Welding trajectory point detection relies on machine vision perception and [...] Read more.
The concept of symmetry is a fundamental principle in various scientific and engineering fields, including welding technology. In the context of this paper, symmetry could play a role in optimizing the welding trajectory. Welding trajectory point detection relies on machine vision perception and intelligent algorithms to extract welding trajectory, which is crucial for the automatic welding of steel parts. However, in practice, sieve-net welding still relies on manual or semi-automatic operations, which have limitations, such as fixed positions and sizes, making it unsafe and inefficient. This paper proposes a 2D skeleton extraction algorithm for detecting weld joints in a sieve-net point cloud. First, the algorithm applies principal component analysis (PCA) to transform the point cloud and projects it into a 2D image with minimal information loss. Second, the expansion corrosion method is then employed to enhance the connectivity and refinement of the sieve-net mesh to serve the extraction of 2D skeleton. Third, the algorithm extracts the skeleton of the sieve-net grid and detects solder points. The average detection accuracy of the proposed algorithm is over 95%, which confirms its feasibility and practical application value in sieve-net welding. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Artificial Intelligence for Point Cloud Data Processing)
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24 pages, 15870 KB  
Article
A Trace Recognition of Rock Mass Point Clouds by the Fusion of Normal Tensor Voting and a Minimum Spanning Tree
by Xijiang Chen, Yi Yang, Qing An and Xianquan Han
Symmetry 2025, 17(3), 415; https://doi.org/10.3390/sym17030415 - 10 Mar 2025
Viewed by 814
Abstract
Point cloud data are often accompanied by noise and irregularities, which bring great challenges to the extraction of point cloud surface traces of discontinuous rock masses. Most of the existing feature line extraction methods rely on traditional geometric or statistical techniques, which are [...] Read more.
Point cloud data are often accompanied by noise and irregularities, which bring great challenges to the extraction of point cloud surface traces of discontinuous rock masses. Most of the existing feature line extraction methods rely on traditional geometric or statistical techniques, which are less resistant to noise. To address this issue, this paper proposes a novel method for trajectory recognition on discontinuous surfaces of rock mass point clouds. The method first detects and extracts the trajectory feature points using normal tensor voting theory based on the symmetry of the point cloud at different periods. Then, three steps of grouping, trace segment growth, and inter-group connection are used to extract discontinuous traces from the feature points. The experimental results show that the optimal triangular grid cell size in this paper is between 5 cm and 7 cm; the optimal range of the angle threshold is between 70° and 90°; the optimal range of the angle threshold is between 50° and 60°; and the value of the distance threshold should be at least 15 times the size of the triangular grid cell. The method in this paper can still maintain a high accuracy and stability in noisy rock mass point cloud data, and has a strong potential for practical application. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Artificial Intelligence for Point Cloud Data Processing)
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