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Algorithm for Point Cloud Compression based on Geometrical Features

Volume 15, Number 3, March 2019, pp. 782-791
DOI: 10.23940/ijpe.19.03.p7.782791

Shiquan Qiao, Kun Zhang, and Kai Gao

School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China 

(Submitted on October 23, 2018; Revised on November 24, 2018; Accepted on December 27, 2018)


As a common and important form, point cloud data exists in computer graphics, especially for 3D visualization. However, with the development of 3D scanning technology, huge data sets have become a main burden in the data processing of point clouds. Therefore, the technology of point cloud compressing is a key content in data pre-processing. This paper provides a new algorithm to compress the point cloud data set. The compressing algorithm can be carried out based on the feature of measure objects. In order to find the data feature, we firstly introduce a point cloud compressing model based on conicoid according to the measure objects. Secondly, for the comparison of the features between the model and the point cloud, we provide a shape operator and a contour operator based on the estimation of geometrical features. Then, according to the value of the shape operator and the contour operator, we provide a matching model. The compressing data algorithm can be created through the matching computation of geometrical features. At last, we use the experiment to prove the feasibility of compressing algorithm, and compare the result of the proposed algorithm and the result of other algorithms in terms of the running time and the compressing effect.


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