Learning of 3D Graph Convolution Networks for Point Cloud Analysis

Point clouds are among the popular geometry representations in 3D vision. However, unlike 2D images with pixel-wise layouts, such representations containing unordered data points which make the processing and understanding the associated semantic information quite challenging. Although a number of p...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 8; pp. 4212 - 4224
Main Authors: Lin, Zhi-Hao, Huang, Sheng-Yu, Wang, Yu-Chiang Frank
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
3D classification
3D segmentation
3D vision
Ablation
Convolution
Data points
deformable kernels
Feature extraction
graph convolution networks
Image segmentation
Kernel
point clouds
Representations
Scale invariance
Shape
Task analysis
Three dimensional models
Three-dimensional displays
Two dimensional displays
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Point clouds are among the popular geometry representations in 3D vision. However, unlike 2D images with pixel-wise layouts, such representations containing unordered data points which make the processing and understanding the associated semantic information quite challenging. Although a number of previous works attempt to analyze point clouds and achieve promising performances, their performances would degrade significantly when data variations like shift and scale changes are presented. In this paper, we propose 3D graph convolution networks (3D-GCN) , which uniquely learns 3D kernels with graph max-pooling mechanisms for extracting geometric features from point cloud data across different scales. We show that, with the proposed 3D-GCN, satisfactory shift and scale invariance can be jointly achieved. We show that 3D-GCN can be applied to point cloud classification and segmentation tasks, with ablation studies and visualizations verifying the design of 3D-GCN.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3059758