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Academic Journal of Computing & Information Science, 2021, 4(7); doi: 10.25236/AJCIS.2021.040710.

Research and Implementation of Compression Algorithm for Large-scale Point Cloud Data

Author(s)

Jinlei Liang1,2

Corresponding Author:
Jinlei Liang
Affiliation(s)

1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

2Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China

Abstract

The amount of original point cloud data obtained by 3D scanner is very large and has a lot of redundant information, which is not conducive to the later data processing. Therefore, it is necessary to compress the original point cloud data.In this paper, an improved curvature-graded point cloud compression method based on hierarchical clustering is proposed.First, hierarchical clustering is performed according to Gaussian curvature and included angle,then curvature classification is performed, and finally point cloud compression is performed according to sampling ratio and distance threshold.The experimental results show that the proposed method has advantages.

Keywords

Point cloud compression, curvature grading, hierarchical clustering, gaussian curvature

Cite This Paper

Jinlei Liang. Research and Implementation of Compression Algorithm for Large-scale Point Cloud Data. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 7: 67-72. https://doi.org/10.25236/AJCIS.2021.040710.

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