<abstract xmlns="http://www.w3.org/1999/xhtml"><p>3D point cloud segmentation is one of the key steps in point cloud processing, which is the technology and process of dividing the point cloud data set into several specific regions with unique properties and proposing interesting targets. It has important applications in medical image processing, industrial inspection, cultural relic’s identification and 3D visualization. Despite widespread use, point cloud segmentation still faces many challenges because of uneven sampling density, high redundancy, and lack explicit structure of point cloud data. The main goal of this paper is to analyse the most popular algorithms and methodologies to segment point clouds. To facilitate analysis and summary, according to the principle of segmentation we divide the 3D point cloud segmentation methods into edge-based methods, region-based methods, graph-based methods, model-based methods, and machine learning-based methods. Then analyze and discuss the advantages, disadvantages and application scenarios of these segmentation methods. For some algorithms the results of the segmentation and classification is shown. Finally, we outline the issues that need to be addressed and important future research directions.</p></abstract>

3D point cloud segmentation is one of the key steps in point cloud processing, which is the technology and process of dividing the point cloud data set into several specific regions with unique properties and proposing interesting targets. It has important applications in medical image processing, industrial inspection, cultural relic’s identification and 3D visualization. Despite widespread use, point cloud segmentation still faces many challenges because of uneven sampling density, high redundancy, and lack explicit structure of point cloud data. The main goal of this paper is to analyse the most popular algorithms and methodologies to segment point clouds. To facilitate analysis and summary, according to the principle of segmentation we divide the 3D point cloud segmentation methods into edge-based methods, region-based methods, graph-based methods, model-based methods, and machine learning-based methods. Then analyze and discuss the advantages, disadvantages and application scenarios of these segmentation methods. For some algorithms the results of the segmentation and classification is shown. Finally, we outline the issues that need to be addressed and important future research directions.

Review of 3D Point Cloud Data Segmentation Methods

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Review of 3D Point Cloud Data Segmentation Methods"}

3D point cloud segmentation is one of the key steps in point cloud processing, which is the technology and process of dividing the point cloud data set into...

segmentation methods	Advantage	Disadvantage
edge-based methods	Can detect the edges of different areas very intuitively for point cloud.	sensitive to noise and not suitable for objects with smooth surface changes.
region-based methods	More accurate than edge-based methods.	The segmentation result depends on the quality of the seeds and the merging rules. There will be over-segmentation or under-segmentation.
model-based methods	Fast segmentation speed, and heterogeneous,suitable for simple geometric models.	Difficult to use in complex scenarios.
graph-based methods	Suitable for complex scenes.	Lack of real-time.
machine learning-based methods.	Point cloud segmentation has high accuracy, good recognition effect.	lack of real-time.

Review of 3D Point Cloud Data Segmentation Methods

Publié en ligne: 23 févr. 2022

Pages: 66 - 71

DOI: https://doi.org/10.21307/ijanmc-2020-010

Mots clés
Line Point Cloud, Segmentation, Classification

© 2020 Xiaoyi Ruan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

COMPARISON OF VARIOUS POINT CLOUD SEGMENTATION METHODS

Review of 3D Point Cloud Data Segmentation Methods

Publié en ligne: 23 févr. 2022

Pages: 66 - 71

DOI: https://doi.org/10.21307/ijanmc-2020-010

Mots clésLine Point Cloud, Segmentation, Classification

© 2020 Xiaoyi Ruan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

COMPARISON OF VARIOUS POINT CLOUD SEGMENTATION METHODS

Mots clés
Line Point Cloud, Segmentation, Classification