Research on Classification Method of 3D Point Cloud Model Based on KE-PointVNet

• Published in: Journal of physics. Conference series Vol. 1966; no. 1; pp. 12023 - 12034
• Main Authors: Cheng, Haixia, Zhao, Bo, Zhang, Zhiyu, Liu, Xunjun
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.07.2021
• Subjects: Algorithms; Classification; Cloud computing; Computer vision; Feature extraction; Semantics; Three dimensional models
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1966/1/012023

Abstract With the popularization and use of three-dimensional data acquisition equipment, the acquisition of three-dimensional data is more convenient. Three-dimensional data contains rich shape and scale information. How to effectively and accurately classify, segment and identify three-dimensional data is a research hotspot in the field of computer vision. Aiming at the particularity of 3D point cloud data and the neglect of local correlation between points, this paper proposes a new end-to-end depth network framework, namely KE-PointVNet. KE-PointVNet can directly deal with point cloud and construct deep convolutional network. KE-PointVNet extracts local geometric features of each point based on the EdgeConv module. By using the KNN proximity algorithm to construct local neighborhood of the point cloud, it can avoid its non-local diffusion, and finally obtain the high-level semantics of the point cloud through the local aggregation vector VLAD layer. The experimental results show that this method has a higher classification accuracy than most of the existing point cloud classification methods.