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http://bura.brunel.ac.uk/handle/2438/25071
Title: | DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV |
Authors: | Song, W Liu, Z Guo, Y Sun, S Zu, G Li, M |
Keywords: | semantic segmentation;polar BEV;LiDAR point cloud;dynamic graph convolution network |
Issue Date: | 8-Aug-2022 |
Publisher: | MDPI AG |
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. |
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% |
Description: | Data Availability Statement: Not applicable. |
URI: | https://bura.brunel.ac.uk/handle/2438/25071 |
DOI: | https://doi.org/10.3390/rs14153825 |
Other Identifiers: | 3825 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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