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Three-dimensional model classification method based on shape features and convolutional neural network

A convolutional neural network and 3D model technology, applied in the field of 3D model classification, can solve problems such as 3D model classification, and achieve high classification accuracy and good rotation robustness

Pending Publication Date: 2021-07-02
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0003] In order to solve the problem of 3D model classification, the present invention discloses a 3D model classification method based on shape features and convolutional neural network

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  • Three-dimensional model classification method based on shape features and convolutional neural network

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Embodiment Construction

[0027] The specific implementation manner of the present invention will be further described in detail below in conjunction with specific examples.

[0028] The present invention implements the three-dimensional model classification method based on shape feature and convolutional neural network, and the present invention comprises the following steps:

[0029] Step 1 In order to construct the geometric features of the 3D model, read the data file of the 3D model, discretize the 3D model, and make the surface of the model triangular.

[0030] Step 1-1 reads the 3D model file by using the parsing tool.

[0031] Step 1-2 Use the triangulation tool to triangulate the 3D model, use the file analysis tool to read and triangulate the 3D model sample, and store it in the list file.

[0032] Step 2 Sampling several random points on the surface of the 3D model, selecting N random points, and calculating the Euclidean distance between each random point and the model particle, obtaining ...

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Abstract

The invention relates to a three-dimensional model classification method based on shapes and a convolutional neural network. According to the method, shape feature vectors D1 and D2 of a three-dimensional model are extracted and used for expressing shape features of the three-dimensional model, the shape features are regarded as a vector sequence to serve as input to be transmitted to a convolutional neural network, the convolutional neural network conducts feature extraction on the feature vectors, and finally classification of the three-dimensional model is completed through a Softmax classification layer. The method has a good effect in the aspect of three-dimensional model classification.

Description

Technical field: [0001] The invention relates to a three-dimensional model classification method based on a shape feature and a convolutional neural network, and the method has better application in three-dimensional model classification. Background technique: [0002] In recent years, with the continuous and rapid development of 3D modeling technology, imaging technology and computer vision, the types and quantities of 3D models have experienced explosive growth. How to effectively classify and manage these 3D models has become an urgent problem to be solved, and the classification of 3D models has also attracted the attention of many scholars and industries. Neural networks have been widely used in many fields such as speech recognition, language modeling, machine translation, character recognition and 3D model classification. There are some deficiencies in the classification of 3D models by traditional neural networks. When the 3D models are numerous and complex, the cl...

Claims

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Application Information

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IPC IPC(8): G06T17/20G06K9/62G06N3/04
CPCG06T17/20G06N3/045G06F18/24
Inventor 关小蕊宋文博李靖宇
Owner HARBIN UNIV OF SCI & TECH
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