Methods › Computer Vision › Point Cloud Models › Panoptic-PolarNet
Panoptic-PolarNet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Panoptic-PolarNet is a point cloud segmentation framework for LiDAR point clouds. It learns both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling the authors to circumvent the issue of occlusion among instances in urban street scenes. We first encode the raw point cloud data with K features into a fixed-size representation on the polar BEV map. Next, we use a single backbone encoder-decoder network to generate semantic prediction, center heatmap and offset regression. Finally, we merge these outputs via a voting-based fusion to yield the panoptic segmentation result.
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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Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation 27 Mar 2021 · 2 repositories · arXiv:2103.14962Syntology ran 6 of 11 samples · 5 unverified
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 |
|---|---|
| Clustering | 1 |
| Instance Segmentation | 1 |
| Panoptic Segmentation | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 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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