3D Object Detection On V2X Sim
Métriques
mAOE
mAP
mASE
mATE
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | mAOE | mAP | mASE | mATE | Paper Title | Repository |
|---|---|---|---|---|---|---|
| V2X-ViT | 0.383 | 22.4 | 0.250 | 0.848 | V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer | |
| Where2comm | 0.310 | 19.0 | 0.275 | 0.911 | Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps | |
| V2VNet | 0.349 | 21.4 | 0.255 | 0.768 | V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction | |
| DiscoNet | 0.411 | 22.0 | 0.267 | 0.787 | Learning Distilled Collaboration Graph for Multi-Agent Perception | |
| QUEST | 0.390 | 23.9 | 0.259 | 0.832 | QUEST: Query Stream for Practical Cooperative Perception |
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