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 different 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.

翻译：激光雷达点云上采样对于机器人和自动驾驶车辆的感知系统而言是一项具有挑战性的任务，这主要是因为大规模场景上下文存在稀疏且不规则的结构。近期研究试图通过将激光雷达数据从3D欧几里得空间转换为2D图像空间中的图像超分辨率问题来解决这一难题。尽管这些方法能够生成具有精细细节的高分辨率距离图像，但由此产生的3D点云往往会出现细节模糊并预测出无效点。本文提出了一种新方法TULIP，可以从低分辨率激光雷达输入中重建高分辨率点云。我们也采用基于距离图像的方法，但对基于Swin-Transformer网络的块和窗口几何结构进行了针对性修改，以更好地适配距离图像的特性。我们在三个不同的公开真实场景数据集和模拟数据集上进行了多项实验。TULIP在所有相关指标上均优于现有最先进方法，并且相比先前工作能够生成更鲁棒、更真实的点云。