Methods › Computer Vision › Point Cloud Models › Point-GNN
Point-GNN
Introduced by Weijing Shi et al. in Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Point-GNN is a graph neural network for detecting objects from a LiDAR point cloud. It predicts the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, there is an auto-registration mechanism to reduce translation variance, as well as a box merging and scoring operation to combine detections from multiple vertices accurately.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud 2 Mar 2020 · 1 repository · arXiv:2003.01251
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| 3D Object Detection | 1 |
| Graph Neural Network | 1 |
| Object Detection | 1 |
| Translation | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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