Papers › A Hierarchical Graph Network for 3D Object Detection on Point Clouds
A Hierarchical Graph Network for 3D Object Detection on Point Clouds
Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, Danny Z. Chen, Jian Wu
3D object detection on point clouds finds many applications. However, most known point cloud object detection methods did not adequately accommodate the characteristics (e.g., sparsity) of point clouds, and thus some key semantic information (e.g., shape information) is not well captured. In this paper, we propose a new graph convolution (GConv) based hierarchical graph network (HGNet) for 3D object detection, which processes raw point clouds directly to predict 3D bounding boxes. HGNet effectively captures the relationship of the points and utilizes the multi-level semantics for object detection. Specially, we propose a novel shape-attentive GConv (SA-GConv) to capture the local shape features, by modelling the relative geometric positions of points to describe object shapes. An SA-GConv based U-shape network captures the multi-level features, which are mapped into an identical feature space by an improved voting module and then further utilized to generate proposals. Next, a new GConv based Proposal Reasoning Module reasons on the proposals considering the global scene semantics, and the bounding boxes are then predicted. Consequently, our new framework outperforms state-of-the-art methods on two large-scale point cloud datasets, by 4% mean average precision (mAP) on SUN RGB-D and by 3% mAP on ScanNet-V2.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | SUN-RGBD val | HGNet (Geo only) | mAP@0.25 | 61.6 | #18 of 32 | Archive leaderboard | report |
| 3D Object Detection | ScanNetV2 | HGNet | mAP@0.25 | 61.3 | #27 of 33 | Archive leaderboard | report |
| 3D Object Detection | ScanNetV2 | HGNet | mAP@0.5 | 34.4 | #27 of 33 | 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.
Methods
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