Zhang, W.J., Su, S.Z., Hong, Q.Q. et al. (2 more authors) (2023) Long short‐distance topology modelling of 3D point cloud segmentation with a graph convolution neural network. IET Computer Vision, 17 (3). pp. 251-264. ISSN 1751-9632
Abstract
3D point cloud segmentation is a non-trivial problem due to its irregular, sparse, and unordered data structure. Existing methods only consider structural relationships of a 3D point and its spatial neighbours. However, the inner-point interactions and long-distance context of a 3D point cloud have been less investigated. In this study, we propose an effective plug-and-play module called the Long Short-Distance Topologically Modelled (LSDTM) Graph Convolutional Neural Network (GCNN) to learn the underlying structure of 3D point clouds. Specifically, we introduce the concept of subgraph to model the contextual-point relationships within a short distance. Then the proposed topology can be reconstructed by recursive aggregation of subgraphs, and importantly, to propagate the contextual scope to a long range. The proposed LSDTM can parse the point cloud data with maximisation of preserving the geometric structure and contextual structure, and the topological graph can be trained end-to-end through a seamlessly integrated GCNN. We provide a case study of triple-layer ternary topology and experimental results on ShapeNetPart, Stanford 3D Indoor Semantics and ScanNet datasets, indicating a significant improvement on the task of 3D point cloud segmentation and validating the effectiveness of our research.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Dec 2022 11:03 |
Last Modified: | 26 Sep 2024 10:11 |
Status: | Published |
Publisher: | Institution of Engineering and Technology (IET) |
Refereed: | Yes |
Identification Number: | 10.1049/cvi2.12160 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194549 |