Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN

Chai, Keng Yip; Stenzel, Jonas; Jost, Jana

doi:10.1109/irce50905.2020.9199248

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Then on this basis, the direct point cloud processing method has been proposed, which has changed the problems with rasterization processing. This method mainly contains the definition of graph convolution algorithm and its realization on 3D point cloud data, wherein the two most important deep learning methods are PointNet [12], PointNet++ [13], and the subsequent improved algorithms of the model [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud

Fan¹,

Zhao²,

Su³

et al. 2023

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Regarding the classification of 3D point clouds, existing Graph Convolution Networks (GCN) often fail to effectively learn the correlation of visual features of different scales under the condition of multi-view (multi-domain), thus the features learnt by models are not as varied and the classification accuracy is usually limited. In view of these defects, this paper proposed a Multi-View Deep Visual Adaptive Graph Convolution Network (MVDVAGCN) which integrates the theory of visual selective attention with the graph convolution calculation method and inherits the advantages of the idea of rasterization. In the paper, the deep learning technology, the idea of multi-view, and the VAGCN were combined to establish three GCN models which were then applied to the classification of 3D point clouds and attained good results. Then the parameters set for the proposed algorithm were verified based on the ModelNet40 dataset, the recognition performance and geometrical invariance of the proposed algorithm and a few reference algorithms including VoxNeT, PointNet, PointNet++, DGCNN, KPConv3D-GCN, Dynamic Graph CNN, and 3D_RFGCN, were tested, and the results proved the feasibility, recognition performance, and geometrical invariance of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud

Fan¹,

Zhao²,

Su³

et al. 2023

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“…The direct data processing technique involves immediately applying the graph convolution analysis approach to the 3D point cloud data without first subjecting it to voxel filtering or multi-view conversion. The two most crucial deep learning techniques are PointNet [11], PointNet++ [12], and their enhanced algorithms that followed [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Graph Convolution Algorithm Based on Visual Selectivity and Point Cloud Analysis Application

Zhao¹,

Su²,

Ye³

et al. 2022

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The graph convolution algorithm currently suffers from the drawback of not fusing point cloud information and point cloud topology structure information based on visual selectivity features and using absolute quantities like distance as features, resulting in the algorithm losing geometric invariance. This information serves as the foundation for the "Graph Convolution Algorithm Based on Visual Selectivity and Application of Point Cloud Analysis". In order to propose a graph convolutional kernel and its design method based on visual selectivity, the algorithm analyzes the global characteristics of the point cloud "close in the vicinity and sparse in the distance," the local selectivity of the point cloud topology structure in the neighborhood, and the consistency between features and visual selectivity of primates. By combining point cloud information with point cloud topology structure information features, a graph convolution computation method was built, and the algorithm's geometric invariance was confirmed. The recognition and semantic segmentation performances of the approach in this study were verified using the ModelNet40 and ShapeNetPart data sets in comparison to the PointNet, PointNet++, DGCNN, KPConv, and 3D-GCN algorithms. The experimental design demonstrates that the algorithm presented in this research is accurate and practical, has geometric invariance, and performs better at semantic segmentation and recognition than conventional algorithms.

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Fast Prediction of Structural Stress Field Using Point Cloud Deep Learning

Yang,

Wang,

et al. 2024

Mechanisms and Machine Science

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Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN

Cited by 3 publications

References 12 publications

The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud

The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud

Graph Convolution Algorithm Based on Visual Selectivity and Point Cloud Analysis Application

Fast Prediction of Structural Stress Field Using Point Cloud Deep Learning

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