  Motivated by the increasing application of low-resolution LiDAR recently, we
target the problem of low-resolution LiDAR-camera calibration in this work. The
main challenges are two-fold: sparsity and noise in point clouds. To address
the problem, we propose to apply depth interpolation to increase the point
density and supervised contrastive learning to learn noise-resistant features.
The experiments on RELLIS-3D demonstrate that our approach achieves an average
mean absolute rotation/translation errors of 0.15cm/0.33\textdegree on
32-channel LiDAR point cloud data, which significantly outperforms all
reference methods.
