  We introduce a novel architecture, UniCal, for Camera-to-LiDAR (C2L)
extrinsic calibration which leverages self-attention mechanisms through a
Transformer-based backbone network to infer the 6-degree of freedom (DoF)
relative transformation between the sensors. Unlike previous methods, UniCal
performs an early fusion of the input camera and LiDAR data by aggregating
camera image channels and LiDAR mappings into a multi-channel unified
representation before extracting their features jointly with a single-branch
architecture. This single-branch architecture makes UniCal lightweight, which
is desirable in applications with restrained resources such as autonomous
driving. Through experiments, we show that UniCal achieves state-of-the-art
results compared to existing methods. We also show that through transfer
learning, weights learned on the calibration task can be applied to a
calibration validation task without re-training the backbone.
