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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