3D Point Cloud Scene Data Acquisition and Its Key Technologies for Scene Understanding

Yong, 李勇 Li; Guofeng, 佟国峰 Tong; Jingchao, 杨景超 Yang; Liqiang, 张立强 Zhang; Hao, 彭浩 Peng; Huashuai, 高华帅 Gao

doi:10.3788/lop56.040002

Cited by 10 publications

(5 citation statements)

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“…A 3D point cloud is a file obtained by scanning the surface of a workpiece with a laser displacement sensor. The position of each point in the point cloud is described by a set of Cartesian coordinates ( X , Y , Z ) 14 . The 3D point cloud file can be processed to obtain many geometrical features of the workpiece surface.…”

Section: Methodsmentioning

confidence: 99%

“…The position of each point in the point cloud is described by a set of Cartesian coordinates (X,Y,Z). 14 The 3D point cloud file can be processed to obtain many geometrical features of the workpiece surface. Curvature is a physical measure of the degree of bending, and the formation of defects on the surface of a compound material, such as bumps, depressions, or even faults on the normal surface of the part, can alter the curvature of the point cloud surface.…”

Section: Feature Selectionmentioning

confidence: 99%

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Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network

Wang,

Zhang,

et al. 2023

Polymer Composites

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In the aerospace sector, the identification and extraction of surface defects on composite parts is particularly important as they can have a very serious impact. This paper proposes a new extraction method for surface defects of composite materials, where 3D point clouds on the surface of composite workpieces are directly manipulated to finally extract a point cloud of defects containing both morphological and spatial location information. The specific operation explores the influencing factors in the formation and extraction process of composite surface defects and summarizes them into five categories: material type, fiber direction, curvature algorithm, neighborhood size, and filtering threshold. The Multilayer Perceptron‐Moth Flame algorithm two‐stage network model was constructed, which can reveal the relationship between these five types of influencing factors and the formation and extraction of surface defects in composites in the forward direction with an accuracy of 93.07%. It can also achieve optimal parameter recommendation in the reverse direction.Highlights Automatic identification and extraction of defects on the surface of composite workpieces is achieved based on 3D point cloud and curvature calculation. The forward direction reveals the relationship between the surface defect formation process and the material type and fabric direction. The reverse direction can automatically select the best curvature algorithm, neighborhood size, and filtering condition parameters. Build a Multilayer Perceptron‐Moth Flame Algorithm network structure based on multilayer perceptron and moth‐flame algorithm for model training of defect extraction, with an accuracy of 93.07%.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network

Wang,

Zhang,

et al. 2023

Polymer Composites

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show abstract

“…In 2009, Aleksey Golovinskiy proposed a point cloud segmentation algorithm based on the minimum cut value. Given the target position, this method constructs the knearest neighbor graph, assumes the background in advance, sets constraints on the foreground (optional background), and calculates the forest background segmentation scheme by finding the minimum cut [15].…”

Section: Graph -Based Segmentation Methodsmentioning

confidence: 99%

A Review of Segmentation Technology Based on 3D Point Cloud

Wang

Liu

2021

International Journal of Advanced Network, Monitoring and Controls

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Point cloud is an important data format that can completely express scene information. In recent years, with the dramatic increase of polygon complexity of model, the advantage of point model becomes more and more obvious. The point-based computer graphics, which takes point cloud as the research object, has aroused the attention of more and more people, and has been widely used in many fields such as computer vision, automatic driving and robot. In this paper, the point cloud segmentation methods and research status were reviewed in detail, the representative algorithms of each type of methods were analyzed and introduced, and the basic ideas, advantages and disadvantages of each type of methods were summarized. Finally, the problems faced by the segmentation technology and the development direction of the future work are prospected.

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“…LiDAR technology, while not new, has gained renewed interest due to the surge of machine learning-based techniques [9]. The research on the crosscut between LiDAR and machine learning is multi-faceted, emphasising the need to collate and analyse literature on point clouds within the railway monitoring and predictive maintenance domain.…”

Section: Introductionmentioning

confidence: 99%

“…Different datasets of the railway environment are discussed in [12]. Techniques for point cloud analysis are reviewed in [9] and [13]. However, there seems to be a gap in systematic reviews specifically targeting point cloud segmentation or object detection methods.…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Dekker,

Ton,

Meijer

et al. 2023

IEEE Access

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3D Point Cloud Scene Data Acquisition and Its Key Technologies for Scene Understanding

Cited by 10 publications

References 0 publications

Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network

Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network

A Review of Segmentation Technology Based on 3D Point Cloud

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

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