=Three-dimensional point cloud model classification method based on convolution neural network

Abstract

The invention discloses a three-dimensional point cloud model classification method based on convolution neural network, includes selecting Princeton ModelNet to generate training set and data set from training data and test data by selecting required number of models from official website according to ModelNet 10 and ModelNet 40 respectively, selecting training data and test data from official website according to Princeton ModelNet, selecting Princeton ModelNet to generate training set and data set according to model Net 10 and ModelNet 40 respectively, and selecting Princeton ModelNet to generate training data and test data. 2, carry out feature analysis on that point cloud model and constructing a classification framework; S3, ordering the point cloud; S4, two-dimensional visualizing the ordered point cloud data; S5, Constructing CNN network for two-dimensional point cloud image. The invention applies the CNN in the image field directly to the classification of the three-dimensional point cloud model for the first time, 93.97% and 89.75% classification accuracy were obtained on ModelNet 10 and ModelNet 40 respectively, Experimental results show that it is feasible to classify 3D point cloud model by using CNN in image domain. PCI2CNN proposed in this paper can capture 3D feature information of point cloud model effectively and is suitable for classification of 3D point cloud model.

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 Various application research of 3D point cloud data. Among them, the classification of point cloud data is the basis and key of various application 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 deep learning model...

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Problems solved by technology

[0005] Through analysis, it is found that the above work focuses on how to solve the irregularity and disorder of point cloud data, and tries to introduce T-Net, X-transform, and symmetric functions to solve these problems, and has indeed achieved certain results. However, these works It is necessary to design a special network for the point cloud model, and it is impossible to apply the convolutional neural network (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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