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CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis.
In: Sensors (14248220), Jg. 22 (2022-07-01), Heft 13, S. 4878-4891
Online
academicJournal
Zugriff:
Point cloud processing based on deep learning is developing rapidly. However, previous networks failed to simultaneously extract inter-feature interaction and geometric information. In this paper, we propose a novel point cloud analysis module, CGR-block, which mainly uses two units to learn point cloud features: correlated feature extractor and geometric feature fusion. CGR-block provides an efficient method for extracting geometric pattern tokens and deep information interaction of point features on disordered 3D point clouds. In addition, we also introduce a residual mapping branch inside each CGR-block module for the further improvement of the network performance. We construct our classification and segmentation network with CGR-block as the basic module to extract features hierarchically from the original point cloud. The overall accuracy of our network on the ModelNet40 and ScanObjectNN benchmarks achieves 94.1% and 83.5%, respectively, and the instance mIoU on the ShapeNet-Part benchmark also achieves 85.5%, proving the superiority of our method. [ABSTRACT FROM AUTHOR]
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CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis.
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Autor/in / Beteiligte Person: | Wang, Fan ; Zhao, Yingxiang ; Shi, Gang ; Cui, Qing ; Cao, Tengfei ; Jiang, Xian ; Hou, Yongjie ; Zhuang, Rujun ; Mei, Yunfei |
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Zeitschrift: | Sensors (14248220), Jg. 22 (2022-07-01), Heft 13, S. 4878-4891 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1424-8220 (print) |
DOI: | 10.3390/s22134878 |
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