Convolutional neural network in the classifying problem of point clouds in three-dimensional space
- Resource Type
- Conference
- Authors
- Makovetskii, Artyom; Kober, Vitaly; Zhernov, Dmitrii; Voronin, Alexei
- Source
- 2021 International Conference on Information Technology and Nanotechnology (ITNT) Information Technology and Nanotechnology (ITNT), 2021 International Conference on. :1-4 Sep, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Point cloud compression
Geometry
Three-dimensional displays
Image analysis
Shape
Neural networks
Image restoration
point cloud
data dimension change
convolutional neural network
fully connected neural network
classification
- Language
Most 3D acquisition devices create point clouds in 3D space. The use of convolutional neural networks (CNN) in image analysis shows the advantage of neural network algorithms over classical algorithms for some types of problems. The success of neural networks in analyzing 2D images has led to adaptation of neural networks for processing of 3D data. Point clouds by their nature do not explicitly contain information about the geometry of an object; therefore, methods that restore the geometric characteristics of a point cloud can enhance the capabilities of neural networks. In the paper, we propose a convolutional neural network for processing point clouds in three-dimensional space. The network classifies objects defined by point clouds. The proposed approach does not depend on the choice of numbering of points in the cloud. With the help of computer simulation, we show the performance of the proposed network on ModelNet40 database.