Calibration-free BEV Representation for Infrastructure Perception. (arXiv:2303.03583v2 [cs.CV] UPDATED)

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 …

applications arxiv cameras detection features free infrastructure natural network paper perception perspective practical representation snow traffic understanding