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DC Field | Value | Language |
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dc.contributor.author | Song, W | - |
dc.contributor.author | Liu, Z | - |
dc.contributor.author | Guo, Y | - |
dc.contributor.author | Sun, S | - |
dc.contributor.author | Zu, G | - |
dc.contributor.author | Li, M | - |
dc.date.accessioned | 2022-08-12T12:26:09Z | - |
dc.date.available | 2022-08-12T12:26:09Z | - |
dc.date.issued | 2022-08-08 | - |
dc.identifier | 3825 | - |
dc.identifier.citation | Song, W. et al. (2022) ‘DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV’, Remote Sensing, 14 (15), 3825, pp. 1 - 18. doi: 10.3390/rs14153825. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25071 | - |
dc.description | Data Availability Statement: Not applicable. | en_US |
dc.description.abstract | Copyright: © 2022 by the authors. Semantic segmentation in LiDAR point clouds has become an important research topic for autonomous driving systems. This paper proposes a dynamic graph convolution neural network for LiDAR point cloud semantic segmentation using a polar bird’s-eye view, referred to as DGPolarNet. LiDAR point clouds are converted to polar coordinates, which are rasterized into regular grids. The points mapped onto each grid distribute evenly to solve the problem of the sparse distribution and uneven density of LiDAR point clouds. In DGPolarNet, a dynamic feature extraction module is designed to generate edge features of perceptual points of interest sampled by the farthest point sampling and K-nearest neighbor methods. By embedding edge features with the original point cloud, local features are obtained and input into PointNet to quantize the points and predict semantic segmentation results. The system was tested on the Semantic KITTI dataset, and the segmentation accuracy reached 56.5% | en_US |
dc.description.sponsorship | This research was supported by the Education and Teaching Reform Project of North China University of Technology, Beijing Urban Governance Research Base, the Ministry of Science (MSIT, ICT), Korea, under the High-Potential Individuals Global Training Program (2020-0-01576) supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP), the Great Wall Scholar Program (CIT&TCD20190304), and the National Natural Science Foundation of China (No. 61503005). | en_US |
dc.format.extent | 1 - 18 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | semantic segmentation | en_US |
dc.subject | polar BEV | en_US |
dc.subject | LiDAR point cloud | en_US |
dc.subject | dynamic graph convolution network | en_US |
dc.title | DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/rs14153825 | - |
dc.relation.isPartOf | Remote Sensing | - |
pubs.issue | 15 | - |
pubs.publication-status | Published online | - |
pubs.volume | 14 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.rights.holder | The authors. | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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