Grunnleggende konsepter
Proposing GraphBEV to address feature misalignment issues in BEV-based methods for 3D object detection.
Sammendrag
The content discusses the challenges of feature misalignment in LiDAR and camera fusion for 3D object detection. It introduces the GraphBEV framework, consisting of LocalAlign and GlobalAlign modules, to enhance alignment between LiDAR and camera BEV features. The framework achieves state-of-the-art performance on the nuScenes dataset, surpassing BEVFusion under noisy misalignment conditions.
Introduction
- Importance of 3D object detection in autonomous driving.
- Multi-modal fusion paradigm like BEVFusion.
- Challenges of feature misalignment due to calibration errors.
Methodology
- Proposal of GraphBEV framework with LocalAlign and GlobalAlign modules.
- Description of how LocalAlign addresses local misalignment using neighbor depth information.
- Explanation of GlobalAlign module for global feature alignment between LiDAR and camera BEV features.
Experiments
- Evaluation on nuScenes dataset showing superior performance compared to baseline methods.
- Robustness analysis under different weather conditions, ego distances, and object sizes.
- Impact analysis of hyperparameter Kgraph on feature alignment.
Statistikk
GraphBEVは、nuScenes検証セットでmAPが70.1%であり、ノイズのある不整合設定では8.3%の改善を示しました。
Sitater
"Our GraphBEV achieves state-of-the-art performance, with an mAP of 70.1%, surpassing BEVFusion by 1.6% on the nuScenes validation set."
"Importantly, our GraphBEV outperforms BEVFusion by 8.3% under conditions with misalignment noise."