Algorithm for Point Cloud Compression based on Geometrical Features

doi:10.23940/ijpe.19.03.p7.782791

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 782-791.doi: 10.23940/ijpe.19.03.p7.782791

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Algorithm for Point Cloud Compression based on Geometrical Features

Shiquan Qiao, Kun Zhang^*, and Kai Gao

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

Submitted on ; Revised on ;
Contact: euphkun@163.com
About author:Shiquan Qiao received his Master's degree in test measurement technology and instruments from Hebei University of Science and Technology in 2011. He works as a lecturer in the School of Information Science and Engineering at Hebei University of Science and Technology. His main research interests include the research and implementation on image processing and algorithm analysis. Kun Zhang received her Ph.D. in circuits and systems and her Master's degree in software theory from Yanshan University in 2016. She works as a lecturer in the School of Information Science and Engineering at Hebei University of Science and Technology. She is also a member of the China Computer Federation (CCF). Her main research interests include computer graphics, machine learning, and applications of intelligent algorithms. Kai Gao received his Ph.D. from Shanghai Jiaotong University. He is a professor in the School of Information Science and Engineering at Hebei University of Science and Technology. He is a senior member of the China Computer Federation (CCF) and a member of Chinese Information Technology (CCF TCCI) & Computer Applications (CCF TCAPP). He is also an associate editor of the EI Compendex indexed UK journal “International Journal of Computer Applications in Technology”. His research interests include artificial intelligence, natural language processing, social network computing, web information retrieval, and big data mining.

Abstract

Abstract: 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.

Key words: point cloud compressing, conicoid, geometrical features, contour feature, matching model

Shiquan Qiao, Kun Zhang, and Kai Gao. Algorithm for Point Cloud Compression based on Geometrical Features [J]. Int J Performability Eng, 2019, 15(3): 782-791.

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Algorithm for Point Cloud Compression based on Geometrical Features

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