Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting

Understand

The emergence of Multi-Camera 3D Object Detection (MC3D-Det), facilitated by bird's-eye view (BEV) representation, signifies a notable progression in 3D object detection.

• Scaling MC3D-Det training effectively accommodates varied camera parameters and urban landscapes, paving the way for the MC3D-Det foundation model.
• However, the multi-view fusion stage of the MC3D-Det method relies on the ill-posed monocular perception during training rather than surround refinement ability, leading to what we term "surround refinement degradation".
• To this end, our study presents a weak-to-strong eliciting framework aimed at enhancing surround refinement while maintaining robust monocular perception.