Geometric Back-Projection Network for Point Cloud Classification
As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the ge...
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| Vydáno v: | IEEE transactions on multimedia Ročník 24; s. 1943 - 1955 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1520-9210, 1941-0077 |
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| Abstract | As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the geometric information of points in low-level 3D space explicitly. On the other hand, we apply CNN-based structures in high-level feature spaces to learn local geometric context implicitly. Specifically, we leverage an idea of error-correcting feedback structure to capture the local features of point clouds comprehensively. Furthermore, an attention module based on channel affinity assists the feature map to avoid possible redundancy by emphasizing its distinct channels. The performance on both synthetic and real-world point clouds datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency. |
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| AbstractList | As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the geometric information of points in low-level 3D space explicitly. On the other hand, we apply CNN-based structures in high-level feature spaces to learn local geometric context implicitly. Specifically, we leverage an idea of error-correcting feedback structure to capture the local features of point clouds comprehensively. Furthermore, an attention module based on channel affinity assists the feature map to avoid possible redundancy by emphasizing its distinct channels. The performance on both synthetic and real-world point clouds datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency. |
| Author | Barnes, Nick Anwar, Saeed Qiu, Shi |
| Author_xml | – sequence: 1 givenname: Shi orcidid: 0000-0001-9958-180X surname: Qiu fullname: Qiu, Shi email: shi.qiu@anu.edu.au organization: Data61, CSIRO (The Commonwealth Scientific and Industrial Research Organisation), and Research School of Engineering, Australian National University, Canberra, ACT, Australia – sequence: 2 givenname: Saeed orcidid: 0000-0002-0692-8411 surname: Anwar fullname: Anwar, Saeed email: saeed.anwar@data61.csiro.au organization: Data61, CSIRO (The Commonwealth Scientific and Industrial Research Organisation), and Research School of Engineering, Australian National University, Canberra, ACT, Australia – sequence: 3 givenname: Nick orcidid: 0000-0002-9343-9535 surname: Barnes fullname: Barnes, Nick email: nick.barnes@anu.edu.au organization: School of Computing, Australian National University, Canberra, ACT, Australia |
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| Snippet | As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we... |
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| SubjectTerms | 3D Deep Learning Attention Mechanism Classification Error correction Error-correcting Feedback Feature extraction Feature maps Geometric Features Geometry Microbalances Point Cloud Classification Redundancy Shape Task analysis Three-dimensional displays Visualization |
| Title | Geometric Back-Projection Network for Point Cloud Classification |
| URI | https://ieeexplore.ieee.org/document/9410405 https://www.proquest.com/docview/2647425697 |
| Volume | 24 |
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