A 3D Point Cloud Model Classification Method Based on Convolutional Neural Network

A technology of convolutional neural network and 3D point cloud, which is applied in the field of 3D point cloud model classification based on convolutional neural network, and can solve application problems

Active Publication Date: 2021-09-14
BEIFANG UNIV OF NATITIES
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AI Technical Summary

Problems solved by technology

However, these works all need to design a special network for the point cloud model, and it is impossible to apply the Convolution Neural Network (CNN), which has achieved great success in the field of image recognition, to the classification of point cloud models.

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  • A 3D Point Cloud Model Classification Method Based on Convolutional Neural Network
  • A 3D Point Cloud Model Classification Method Based on Convolutional Neural Network
  • A 3D Point Cloud Model Classification Method Based on Convolutional Neural Network

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Embodiment Construction

[0054] The present invention will be further described below in conjunction with specific examples.

[0055] The three-dimensional point cloud model classification method based on the convolutional neural network provided in this embodiment mainly designs three methods for ordering three-dimensional point cloud data, three methods for two-dimensional imaging of ordered point cloud data, and is suitable for A convolutional neural network PCI2CNN for two-dimensional point cloud image classification, which specifically includes the following steps:

[0056] S1. Select Princeton ModelNet, select a certain number of models from the official website as training data and test data for ModelNet10 and ModelNet40 respectively, and generate training sets and test sets; specifically, select PrincetonModelNet, use official website data, and select 3991 models for ModelNet10 and ModelNet40 respectively , 9842 models are used as training data, and 908 and 2468 models are used as test data. ...

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Abstract

The invention discloses a three-dimensional point cloud model classification method based on a convolutional neural network, comprising the steps of: S1, selecting Princeton ModelNet, aiming at ModelNet10 and ModelNet40 respectively, selecting a required number of models from the official website as training data and test data, and generating Training set and data set; S2, feature analysis on the point cloud model and construction of a classification framework; S3, ordering the point cloud; S4, two-dimensional image of the ordered point cloud data; S5, constructing a two-dimensional point-oriented CNN network for cloud images. For the first time, the present invention directly applies CNN in the image field to the classification of 3D point cloud models, and achieved classification accuracy rates of 93.97% and 89.75% respectively on ModelNet10 and ModelNet40, which are comparable to the current best methods. The experimental results fully show that, It is feasible to apply CNN in the image field to the classification of 3D point cloud models. The PCI2CNN proposed in this paper can effectively capture the 3D feature information of the point cloud model and is suitable for the classification of 3D point cloud models.

Description

technical field [0001] The invention relates to the technical fields of computer graphics, computer vision and intelligent recognition, in particular to a three-dimensional point cloud model classification method based on a convolutional neural network. Background technique [0002] With the rapid development of modern computer vision research, breakthroughs have been made in areas such as unmanned vehicles, autonomous robots, real-time SLAM technology and virtual 3D models, which has promoted the development of the usability of 3D point cloud data, and has also given birth to the Research on various applications of 3D point cloud data. Among them, the classification of point cloud data is the basis and key of various applied research. [0003] At present, deep learning technology has made breakthroughs in the field of image and speech recognition, which also provides a useful research direction for the classification of 3D models. However, the input that the deep learning...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 白静司庆龙刘振刚
Owner BEIFANG UNIV OF NATITIES
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