Boundary segmentation of salt pile point clouds based on clustering and model fitting

Author:

Chen Zengyan1ORCID,Liu Jun2ORCID,Liu Zhengtao1ORCID,Zhang Yun3ORCID,Pei Shengchang1ORCID

Affiliation:

1. Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology) School of Computer Science and Engineering, Wuhan Institute of Technology Wuhan, China, Wuhan Institute of Technology:Wuhan, China, China

2. Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology) School of Computer Science and Engineering, Wuhan Institute of Technology Wuhan, China, Wuhan Institute of Technology:Wuhan, China, China and Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang province, China, Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang province, China, China

3. Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology) School of Computer Science and Engineering, Wuhan Institute of Technology Wuhan, China, Wuhan Institute of Technology, China

Publisher

ACM

Reference14 articles.

1. A robust statistics approach for plane detection in unorganized point clouds

2. An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds

3. Schaefer A, Vertens J, Büscher D, A maximum likelihood approach to extract finite planes from 3-D laser scans[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 72-78.

4. Charles R. Qi, Hao Su, Kaichum Mo, PointNet: Deep learning on point sets for 3D classifification and segmentation[C]. IEEE Conference on Computer Visionand Pattern Recongnition, 2017.

5. Yu L, Li X, Fu C W, Pu-net: Point cloud upsampling network[C] Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2790-2799.

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