  Effective BEV object detection on infrastructure can greatly improve traffic
scenes understanding and vehicle-toinfrastructure (V2I) cooperative perception.
However, cameras installed on infrastructure have various postures, and
previous BEV detection methods rely on accurate calibration, which is difficult
for practical applications due to inevitable natural factors (e.g., wind and
snow). In this paper, we propose a Calibration-free BEV Representation (CBR)
network, which achieves 3D detection based on BEV representation without
calibration parameters and additional depth supervision. Specifically, we
utilize two multi-layer perceptrons for decoupling the features from
perspective view to front view and birdeye view under boxes-induced foreground
supervision. Then, a cross-view feature fusion module matches features from
orthogonal views according to similarity and conducts BEV feature enhancement
with front view features. Experimental results on DAIR-V2X demonstrate that CBR
achieves acceptable performance without any camera parameters and is naturally
not affected by calibration noises. We hope CBR can serve as a baseline for
future research addressing practical challenges of infrastructure perception.
