Panoptic Segmentation On S3Dis Area5
Metrics
PQ
Params (M)
RQ
SQ
Results
Performance results of various models on this benchmark
Model Name | PQ | Params (M) | RQ | SQ | Paper Title | Repository |
|---|---|---|---|---|---|---|
| KPConv (Xiang 2023) | 41.8 | 14.2 | 51.5 | 74.7 | A Review of Panoptic Segmentation for Mobile Mapping Point Clouds | |
| PointGroup (Xiang 2023) | 42.3 | 7.7 | 52.0 | 74.7 | A Review of Panoptic Segmentation for Mobile Mapping Point Clouds | |
| MinkowskiNet (Xiang 2023) | 39.2 | 38.0 | 48.0 | 74.9 | A Review of Panoptic Segmentation for Mobile Mapping Point Clouds | |
| SuperCluster | 50.1 | 0.21 | 60.1 | 76.6 | Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering | |
| PointNet++ (Xiang 2023) | 24.6 | 3.1 | 32.6 | 68.2 | A Review of Panoptic Segmentation for Mobile Mapping Point Clouds |
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