Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection
LiDAR-based 3D object detection is fundamental in autonomous driving but remains challenging due to the irregularity, unordered nature, and non-uniform density of point clouds. Existing methods primarily rely on either graph-based or tree-based representations: Graph-based models capture fine-graine...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2177 |
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| author | Jingwen Zhao Jianchao Li Wei Zhou Haohao Ren Yunliang Long Haifeng Hu |
| author_facet | Jingwen Zhao Jianchao Li Wei Zhou Haohao Ren Yunliang Long Haifeng Hu |
| author_sort | Jingwen Zhao |
| collection | DOAJ |
| description | LiDAR-based 3D object detection is fundamental in autonomous driving but remains challenging due to the irregularity, unordered nature, and non-uniform density of point clouds. Existing methods primarily rely on either graph-based or tree-based representations: Graph-based models capture fine-grained local geometry, while tree-based approaches encode hierarchical global semantics. However, these paradigms are often used independently, limiting their overall representational capacity. In this paper, we propose density-aware tree–graph cross-message passing (DA-TGCMP), a unified framework that exploits the complementary strengths of both structures to enable more expressive and robust feature learning. Specifically, we introduce a density-aware graph construction (DAGC) strategy that adaptively models geometric relationships in regions with varying point density and a hierarchical tree representation (HTR) that captures multi-scale contextual information. To bridge the gap between local precision and global contexts, we design a tree–graph cross-message-passing (TGCMP) mechanism that enables bidirectional interaction between graph and tree features. The experimental results of three large-scale benchmarks, KITTI, nuScenes, and Waymo, show that our method achieves competitive performance. Specifically, under the moderate difficulty setting, DA-TGCMP outperforms VoPiFNet by approximately 2.59%, 0.49%, and 3.05% in the car, pedestrian, and cyclist categories, respectively. |
| format | Article |
| id | doaj-art-5403be2ff9fc404bbe45efe283568d8a |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-5403be2ff9fc404bbe45efe283568d8a2025-08-20T03:28:37ZengMDPI AGRemote Sensing2072-42922025-06-011713217710.3390/rs17132177Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object DetectionJingwen Zhao0Jianchao Li1Wei Zhou2Haohao Ren3Yunliang Long4Haifeng Hu5School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, ChinaLiDAR-based 3D object detection is fundamental in autonomous driving but remains challenging due to the irregularity, unordered nature, and non-uniform density of point clouds. Existing methods primarily rely on either graph-based or tree-based representations: Graph-based models capture fine-grained local geometry, while tree-based approaches encode hierarchical global semantics. However, these paradigms are often used independently, limiting their overall representational capacity. In this paper, we propose density-aware tree–graph cross-message passing (DA-TGCMP), a unified framework that exploits the complementary strengths of both structures to enable more expressive and robust feature learning. Specifically, we introduce a density-aware graph construction (DAGC) strategy that adaptively models geometric relationships in regions with varying point density and a hierarchical tree representation (HTR) that captures multi-scale contextual information. To bridge the gap between local precision and global contexts, we design a tree–graph cross-message-passing (TGCMP) mechanism that enables bidirectional interaction between graph and tree features. The experimental results of three large-scale benchmarks, KITTI, nuScenes, and Waymo, show that our method achieves competitive performance. Specifically, under the moderate difficulty setting, DA-TGCMP outperforms VoPiFNet by approximately 2.59%, 0.49%, and 3.05% in the car, pedestrian, and cyclist categories, respectively.https://www.mdpi.com/2072-4292/17/13/2177LiDAR3D object detectionDensity AwareTree–Graph Cross-Message PassingHierarchical Tree Representation |
| spellingShingle | Jingwen Zhao Jianchao Li Wei Zhou Haohao Ren Yunliang Long Haifeng Hu Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection Remote Sensing LiDAR 3D object detection Density Aware Tree–Graph Cross-Message Passing Hierarchical Tree Representation |
| title | Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection |
| title_full | Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection |
| title_fullStr | Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection |
| title_full_unstemmed | Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection |
| title_short | Density-Aware Tree–Graph Cross-Message Passing for LiDAR Point Cloud 3D Object Detection |
| title_sort | density aware tree graph cross message passing for lidar point cloud 3d object detection |
| topic | LiDAR 3D object detection Density Aware Tree–Graph Cross-Message Passing Hierarchical Tree Representation |
| url | https://www.mdpi.com/2072-4292/17/13/2177 |
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