A comprehensive and hybrid approach to automatic and interactive point cloud segmentation using surface variation analysis and HDBSCAN clustering

•A novel four-step segmentation approach is proposed to enhance the accuracy and robustness of 3D point cloud segmentation.•A novel surface variation parameter is introduced and tested for edge point detection.•A comparative analysis of clustering methods for partitioning dense 3D point clouds is pr...

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Bibliographic Details
Published in:Computers & graphics Vol. 132; p. 104403
Main Authors: Sadaoui, Sif Eddine, Qie, Yifan, Anwer, Nabil, Remil, Oussama, Abdi, Imad, Benaldjia, Nouh, Mammeri, Ismail Ahmed
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2025
Subjects:
Clustering
Edge detection
HDBSCAN
Point cloud
Segmentation
Surface variation
HDBSCAN
Point cloud
Segmentation
Clustering
Edge detection
Surface variation
ISSN:0097-8493
Online Access:Get full text
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Summary:•A novel four-step segmentation approach is proposed to enhance the accuracy and robustness of 3D point cloud segmentation.•A novel surface variation parameter is introduced and tested for edge point detection.•A comparative analysis of clustering methods for partitioning dense 3D point clouds is presented.•An interactive, user-friendly graphical user interface (GUI) is developed to enable dynamic, real-time adjustments during segmentation.•The feasibility of the proposed segmentation method is evaluated using diverse point clouds. Segmentation is one of the four main operations involved in processing point clouds for reverse engineering and metrology. Numerous segmentation methods exist, including region growing, attribute clustering, edge detection, and machine learning-based approaches, each with its own strengths and weaknesses. Hybrid approaches, which combine these methods, can often yield improved results. This paper proposes a novel, hybrid, four-step method for segmenting 3D point clouds of mechanical parts, based on surface variation analysis and a clustering technique. The method begins with the evaluation of a surface variation parameter to differentiate edge and non-edge points, followed by threshold-based separation of the edge points. An edge point expansion technique is then introduced to improve segmentation results by enhancing the spatial distinction between edge and surface points, thereby minimizing the sensitivity of the clustering algorithm. Finally, the HDBSCAN clustering method is employed to group the remaining points into distinct clusters representing individual surfaces. The effectiveness of the proposed technique is validated through experiments on synthetic point clouds of mechanical parts, incorporating added noise and density variations. These experiments demonstrate the method's robustness in reverse engineering applications for mechanical components. A measured point cloud is also taken as an example to verify the feasibility of the proposed method. An interactive graphical user interface (GUI) is also developed to facilitate real-time adjustments during the segmentation process. This research significantly contributes to automatic 3D point cloud analysis and supports advancements in Industry 4.0. [Display omitted]
ISSN:0097-8493
DOI:10.1016/j.cag.2025.104403