Papers › PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection
Yanan Zhang, Di Huang, Yunhong Wang
LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness.
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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 | KITTI Cars Easy | PC-RGNN | AP | 89.13% | #11 of 26 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Easy val | PC-RGNN | AP | 90.94 | #4 of 11 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Hard | PC-RGNN | AP | 75.54% | #10 of 25 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Hard val | PC-RGNN | AP | 80.45 | #4 of 10 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Moderate val | PC-RGNN | AP | 81.43 | #6 of 11 | 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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