  LiDAR Upsampling is a challenging task for the perception systems of robots
and autonomous vehicles, due to the sparse and irregular structure of
large-scale scene contexts. Recent works propose to solve this problem by
converting LiDAR data from 3D Euclidean space into an image super-resolution
problem in 2D image space. Although their methods can generate high-resolution
range images with fine-grained details, the resulting 3D point clouds often
blur out details and predict invalid points. In this paper, we propose TULIP, a
new method to reconstruct high-resolution LiDAR point clouds from
low-resolution LiDAR input. We also follow a range image-based approach but
specifically modify the patch and window geometries of a Swin-Transformer-based
network to better fit the characteristics of range images. We conducted several
experiments on three public real-world and simulated datasets. TULIP
outperforms state-of-the-art methods in all relevant metrics and generates
robust and more realistic point clouds than prior works.
