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.

我们介绍了一种新颖的架构 UniCal，用于相机和 LiDAR 之间的外部标定，通过基于 Transformer 的骨干网络利用自注意机制来推断传感器之间的 6 自由度相对变换。与以往的方法不同，UniCal 通过将相机图像通道和 LiDAR 映射聚合到一个多通道统一表示中，再使用单分支架构联合提取特征，从而实现了早期融合。通过实验证明，我们证明 UniCal 相较于现有方法实现了最先进的结果。同时，我们还展示通过迁移学习，可将在标定任务中学习到的权重应用于标定验证任务而无需重新训练骨干网络。