Point cloud registration model and method combining attention mechanism and three-dimensional graph convolutional network

A convolutional network, point cloud registration technology, applied in biological neural network models, image analysis, image data processing and other directions, can solve the problems of long training time and high time complexity of point clouds, saving time, high shape and The effect of size flexibility, information loss reduction

Active Publication Date: 2020-11-03
CAPITAL NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the backbone network of these methods is directly used for point cloud

Method used

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  • Point cloud registration model and method combining attention mechanism and three-dimensional graph convolutional network
  • Point cloud registration model and method combining attention mechanism and three-dimensional graph convolutional network
  • Point cloud registration model and method combining attention mechanism and three-dimensional graph convolutional network

Examples

Experimental program
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Effect test

Embodiment 1

[0050] A point cloud registration model combining attention mechanism and 3D graph convolutional network, as shown in the attached figure 1 As shown, the model is a three-branch Siamese (Siamese) architecture, including a Detector model and a Descriptor model, and the Detector model is used to extract the attention features of points and construct an attention mechanism; the Descriptor model is used to generate The expression of the 3D depth feature is used to represent the 3D depth feature of the point, and learn to distinguish the depth feature of the point cloud.

[0051] The Detector model mainly extracts and generates the attention feature of the point cloud through a graph convolutional network module MLP_GCN based on the spectral domain, and uses 5 complete connection layers (channels: 64, 64, 128, filter device: 1×1) to extract the initial point cloud features, and further realize the point cloud feature extraction function h(·). Then, the point cloud feature (X) is e...

Embodiment 2

[0068] Based on the above-mentioned embodiment 1, the registration method of the above-mentioned point cloud registration model includes the following steps: first perform model training, use feature alignment triplet loss to construct a loss function to train the model, and effectively extract attention features and descriptions from point clouds Descriptor feature; after model training, point cloud registration is performed.

[0069] During the model learning process, the Detector model generates an anchor point a anc =(a 1 ,a 2 ,...,a n ) attention vector. The Descriptor model generates the depth f of the anchor anc =(f anc_1 , f anc_2,fanc_3 ,..., f anc_n ), positive depth feature f pos =(f pos_1 , f pos_2 , f pos_3 ,..., f pos_n ), and the negative depth feature f neg =(f neg_1 , f neg_2 , f neg_3 ,..., f neg_n ). The four feature vectors are combined by feature alignment triplet loss to construct the objective function.

[0070] The construction of fea...

Embodiment 3

[0096] Based on the above-mentioned embodiment 2, it is carried out in a certain environment, using two public data sets, but it does not mean that the invention can only have such performance in this environment or this data set. This calculation example is intended to specifically demonstrate the comparison between this embodiment and other four existing point cloud registration methods.

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Abstract

The invention relates to a point cloud registration model and method combining an attention mechanism and a three-dimensional graph convolutional network, and the model is a three-branch Siamese architecture, and comprises a Dejector model and a Descriptor model. The Detector model is used for extracting attention features of points and constructing an attention mechanism; the Descriptor model isused for generating an expression of a three-dimensional depth feature to represent the three-dimensional depth feature of the point, and learning and judging the depth feature of the point cloud. Themethod comprises the following steps: carrying out model training, and constructing a loss function to train a model by using a failure align triplet loss, so as to effectively extract attention features and descriptor features from a point cloud; after model training, carrying out the point cloud registration. According to the method, the key points and the three-dimensional depth features of each key point can be automatically extracted, in the three-dimensional graph convolutional network, the multi-layer perceptron MLP is combined with the graph convolutional network GCN, a new point cloud feature extraction module is designed, more point cloud features with identification significance can be extracted, and the accuracy of point cloud registration is improved.

Description

technical field [0001] The invention relates to the fields of computer vision and geospatial information science, in particular to a point cloud registration model and method combined with an attention mechanism and a three-dimensional image convolution network. Background technique [0002] 3D point cloud can provide rich and dense object space information, and plays an important role in the fields of civil traffic engineering, tunnel engineering, digital city, simultaneous positioning and surveying and mapping. In these applications, point cloud registration is a basic and critical problem, due to the error of the positioning sensor or the inconsistency of the coordinate system, there is a certain mismatch between the different phases or views of the spatial data, and the complexity and complexity of the spatial object. Local similarity, automatic and efficient registration of cloud points is facing challenges. [0003] The 3D point cloud descriptor includes 3D Harris, 3D...

Claims

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Application Information

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IPC IPC(8): G06T7/33G06K9/62G06N3/04
CPCG06T7/344G06N3/045G06F18/24147G06F18/214
Inventor 张振鑫孙澜钟若飞李小娟宫辉力邹建军
Owner CAPITAL NORMAL UNIVERSITY
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