Lidar Semantic Segmentation On Nuscenes
Métriques
val mIoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | val mIoU |
|---|---|
| lsk3dnet-towards-effective-and-efficient-3d | 0.801 |
| rethinking-range-view-representation-for | - |
| frnet-frustum-range-networks-for-scalable | 0.790 |
| spherical-transformer-for-lidar-based-3d | 0.795 |
| searching-efficient-3d-architectures-with | - |
| 2dpass-2d-priors-assisted-semantic | - |
| using-a-waffle-iron-for-automotive-point | 0.791 |
| perception-aware-multi-sensor-fusion-for-3d | - |
| point-transformer-v2-grouped-vector-attention | 0.802 |
| learning-3d-semantic-segmentation-with-only | - |
| Modèle 11 | - |
| Modèle 12 | - |
| point-transformer-v3-simpler-faster-stronger | 0.812 |
| searching-efficient-3d-architectures-with | - |
| Modèle 15 | - |
| af-2-s3net-attentive-feature-fusion-with | - |
| dino-in-the-room-leveraging-2d-foundation | 0.842 |
| Modèle 18 | - |
| Modèle 19 | - |
| Modèle 20 | - |
| Modèle 21 | - |
| oa-cnns-omni-adaptive-sparse-cnns-for-3d | 0.789 |
| cylinder3d-an-effective-3d-framework-for | - |
| serialized-point-mamba-a-serialized-point | 0.806 |
| Modèle 25 | - |
| Modèle 26 | - |
| point-to-voxel-knowledge-distillation-for-1 | 0.760 |
| polarnet-an-improved-grid-representation-for | - |
| amvnet-assertion-based-multi-view-fusion | - |
| polarstream-streaming-lidar-object-detection | - |
| cylindrical-and-asymmetrical-3d-convolution | - |
| Modèle 32 | - |
| sparse-single-sweep-lidar-point-cloud | - |
| gfnet-geometric-flow-network-for-3d-point | - |
| towards-large-scale-3d-representation | 0.786 |