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Papers › SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds

SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds

7 Jun 2020arXiv:2006.04043archive 2025-07-28

Qingdong He, Zhengning Wang, Hao Zeng, Yi Zeng, Yijun Liu

Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net constructs the local complete graph within each divided 3D spherical voxel and global KNN graph through all voxels. The local and global graphs serve as the attention mechanism to enhance the extracted features. In addition, the novel sparse-to-dense regression module enhances the 3D box estimation accuracy through feature maps aggregation at different levels. Experiments on KITTI detection benchmark demonstrate the efficiency of extending the graph representation to 3D object detection and the proposed SVGA-Net can achieve decent detection accuracy.

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Tasks

3D Object DetectionAutonomous DrivingGraph AttentionObject Detectionobject-detectionregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Object Detection KITTI Cars Easy SVGA-Net AP 87.33% #13 of 26 Archive leaderboard report
3D Object Detection KITTI Cars Easy val SVGA-Net AP 90.59 #5 of 11 Archive leaderboard report
3D Object Detection KITTI Cars Hard SVGA-Net AP 74.63% #11 of 25 Archive leaderboard report
3D Object Detection KITTI Cars Hard val SVGA-Net AP 79.15 #5 of 10 Archive leaderboard report
3D Object Detection KITTI Cars Moderate val SVGA-Net AP 80.23 #7 of 11 Archive leaderboard report
3D Object Detection KITTI Cyclists Easy SVGA-Net AP 79.22% #4 of 12 Archive leaderboard report
3D Object Detection KITTI Cyclists Hard SVGA-Net AP 57.64% #4 of 12 Archive leaderboard report
3D Object Detection KITTI Cyclists Moderate SVGA-Net AP 66.13% #3 of 13 Archive leaderboard report
3D Object Detection KITTI Pedestrians Easy SVGA-Net AP 55.21% #2 of 9 Archive leaderboard report
3D Object Detection KITTI Pedestrians Hard SVGA-Net AP 44.56% #1 of 9 Archive leaderboard report
3D Object Detection KITTI Pedestrians Moderate SVGA-Net AP 47.71% #2 of 12 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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