Research on clustering of non-uniformly distributed point clouds in road scenes-Reference-Cited by-同舟云学术

Research on clustering of non-uniformly distributed point clouds in road scenes

Published:2024-02-02 Issue: Volume: Page:376-381
ISSN:
Container-title:Proceedings of the 2024 16th International Conference on Machine Learning and Computing
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Author:

Lin Li¹^ORCID,Haili Zhao²^ORCID,Zhen Hui¹^ORCID,Rongxin Mi¹^ORCID,Weikuang Li¹^ORCID,Zixuan Chen¹^ORCID

Affiliation:

1. National Computer Network Emergency Response Technical Team/Coordination Center of China, China

2. Changan Communication Technology Co., LTD, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3651671.3651717

Reference21 articles.

1. C. Wang, X. Xiong, H. Yang, X. Liu, L. Liu and S. Sun, "Application of Improved DBSCAN Clustering Method in Point Cloud Data Segmentation," 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Zhuhai, China, 2021, pp. 140-144.

2. M. E. Yabroudi, K. Awedat, R. C. Chabaan, O. Abudayyeh and I. Abdel-Qader, "Adaptive DBSCAN LiDAR Point Cloud Clustering For Autonomous Driving Applications," 2022 IEEE International Conference on Electro Information Technology (eIT), Mankato, MN, USA, 2022, pp. 221-224.

3. An Efficient Class-Constrained DBSCAN Approach for Large-Scale Point Cloud Clustering

4. Zhao, Y., Zhang, X., & Huang, X. (2021), "A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentatio, " in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2464-2473.

5. 3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes