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Academic Journal of Computing & Information Science, 2022, 5(12); doi: 10.25236/AJCIS.2022.051209.

Research on Point Cloud Filtering Data Processing Method Based on Self-adaptive Euclidean Clustering Network


Yunming Liu1, Yuanxiao Liu2

Corresponding Author:
Yuanxiao Liu

1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

2The No.2 Middle School of Qingzhou, Weifang, Shandong, 262500, China


Nowadays, with the development of 3D filtering information processing by data algorithms, people have deeply studied 3D data processing. A series of issues were found in the research process. Moreover, the current research on point cloud data in the use of lidar is not comprehensive. Its research focuses on noise reduction and data block segmentation of point cloud-filtered data. Based on the above background, this paper analyzes the bilateral characteristics of point cloud filtering data in detail. It improves the adaptive clustering network to cluster the sample center points of the point cloud filtering data, effectively improving its readability and processing characteristics.


Point cloud filtering; Self-adaptive algorithm; Euclidean clustering; Convolutional neural network

Cite This Paper

Yunming Liu, Yuanxiao Liu. Research on Point Cloud Filtering Data Processing Method Based on Self-adaptive Euclidean Clustering Network. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 12: 60-66. https://doi.org/10.25236/AJCIS.2022.051209.


[1] J. Yu, C. Yu, C. Lin, and F. Wei, "Improved Iterative Closest Point (ICP) Point Cloud Registration Algorithm based on Matching Point Pair Quadratic Filtering," 2021 International Conference on Computer, Internet of Things and Control Engineering (CITCE), Guangzhou, China, 2021, pp. 1-5.

[2] F. Tang, J. Wang, Z. Wang, and M. Xiao, "Research on Ransac Filtering Optimization Method of Steel Structure Buildings Based on Morphological Features," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 7673-7676.

[3] Q. Wang, L. Wu, Z. Xu, H. Tang, R. Wang, and F. Li, "A progressive morphological filter for point cloud extracted from UAV images," 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 2014, pp. 2023-2026.

[4] H. Zhang, L. Zhu, X. Cai and L. Dong, "Noise removal algorithm based on point cloud classification," 2022 International Seminar on Computer Science and Engineering Technology (SCSET), Indianapolis, IN, USA, 2022, pp. 93-96.

[5] Yerokhin, V. Semenets, A. Nechyporenko, O. Turuta and A. Babii, "F-transform 3D Point Cloud Filtering Algorithm," 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2018, pp. 524-527.

[6] Y. Li, R. Zhang, X. Shao and Y. Xu, "Improved Filtering and Hole Filling Algorithm for the Point Cloud of Rotor Surface Based on PCL," 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China, 2021, pp. 105-109.

[7] L. Ren, J. Tang, C. Cui, R. Song and Y. Ai, "An Improved Cloth Simulation Filtering Algorithm Based on Mining Point Cloud," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), Beijing, China, 2021, pp. 1-4.

[8] S. Yao and J. Shi, "Road 3D Point cloud Data Modeling based on LiDAR," 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), Hangzhou, China, 2022, pp. 465-468.

[9] J. Chen, Y. Lin, Y. Chen, M. Zheng and Y. Zhu, "Simplification and Compression Method Based on Classified Sparse Sampling for LiDAR Point Cloud," 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/ SmartCity/DependSys), Haikou, Hainan, China, 2021, pp. 1389-1396.

[10] M. -H. Le, C. -H. Cheng, D. -G. Liu and T. -T. Nguyen, "Adaptive Two-Stage Filter for De-snowing LiDAR Point Clouds," 2022 International Conference on Control, Robotics and Informatics (ICCRI), Danang, Vietnam, 2022, pp. 38-42.