Three-dimensional point cloud automatic classification method based on graph convolutional neural network

A convolutional neural network and 3D point cloud technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problem of low classification accuracy and achieve the effect of improving classification accuracy

Pending Publication Date: 2021-03-12
BEIJING UNIV OF TECH
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Problems solved by technology

The invention solves the problem of low classification accuracy in the process of three-dimensional point cloud processing, a

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  • Three-dimensional point cloud automatic classification method based on graph convolutional neural network
  • Three-dimensional point cloud automatic classification method based on graph convolutional neural network
  • Three-dimensional point cloud automatic classification method based on graph convolutional neural network

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Embodiment Construction

[0048] The purpose of the present invention is to provide a three-dimensional point cloud automatic classification method based on graph convolutional neural network, which is used for automatic classification and recognition of complex objects in the process of grabbing three-dimensional objects by service robots, so as to facilitate the determination of the grabbing position according to the category information Crawling can complete the training of the network end-to-end without any post-processing. The 3D point cloud automatic classification method of the graph convolutional neural network of the present invention can not only extract local fine-grained features but also integrate contextual semantic information, significantly improving classification accuracy.

[0049] The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate the understanding of t...

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Abstract

A three-dimensional point cloud classification method based on a graph convolutional neural network is used for automatically classifying and recognizing complex objects in the process that a servicerobot grabs the three-dimensional objects, and it is convenient to determine the grabbing position for grabbing according to category information. The invention belongs to the technical field of three-dimensional perception of computer vision and robot navigation. The method comprises the following steps: (1) preprocessing a three-dimensional point cloud; (2) constructing a point cloud classification network; (3) training the constructed network; and (4) performing a classification test by using the trained network. The method has the advantages that (1) the graph convolutional neural networkis adopted to perform local feature learning, so that the learning ability of the network for local topological structure information is enhanced; (2) global feature learning is carried out on the input point cloud, and the understanding of the network on context semantics is enhanced; the local features and the global features are aggregated, and then the classification score of each point cloudis output by using the full connection layer, so that the method has higher classification precision compared with the existing network.

Description

technical field [0001] In the present invention, a three-dimensional point cloud automatic classification method based on graph convolutional neural network is designed, and by constructing a deep learning network based on graph convolution, automatic classification and recognition of complex objects can be realized in the process of grabbing three-dimensional objects by service robots, which is convenient according to The category information determines where to crawl for crawling. The invention relates to the technical field of three-dimensional perception of computer vision and robot navigation, in particular to a method for automatic classification and recognition of three-dimensional point clouds. Background technique [0002] With the development of theories such as computer vision and artificial intelligence, 3D point clouds are widely used and play an important role in the fields of automotive autonomous driving, robot perception and navigation, and virtual / augmented...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/048G06N3/045G06F18/24G06F18/253G06F18/214
Inventor 王亮李建书范德巧
Owner BEIJING UNIV OF TECH
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