Lidar sensors are costly yet critical for understanding the 3D environment in autonomous driving. High-resolution sensors provide more details about the surroundings because they contain more vertical beams, but they come at a much higher cost, limiting their inclusion in autonomous vehicles. Upsampling lidar pointclouds is a promising approach to gain the benefits of high resolution while maintaining an affordable cost. Although there exist many pointcloud upsampling frameworks, a consistent comparison of these works against each other on the same dataset using unified metrics is still missing. In the first part of this paper, we propose to benchmark existing methods on the Kitti dataset. In the second part, we introduce a novel lidar upsampling model, HALS: Height-Aware Lidar Super-resolution. HALS exploits the observation that lidar scans exhibit a height-aware range distribution and adopts a generator architecture with multiple upsampling branches of different receptive fields. HALS regresses polar coordinates instead of spherical coordinates and uses a surface-normal loss. Extensive experiments show that HALS achieves state-of-the-art performance on 3 real-world lidar datasets.

翻译：Lidar 传感器成本昂贵,但对于了解自主驾驶的3D环境而言却至关重要。高分辨率传感器提供了更多关于周围环境的详细信息,因为它们含有更多的垂直光束,但成本高得多,限制了它们进入自主车辆。对利达尔点球球进行抽查是一种大有希望的方法,既能从高分辨率中获益,同时又能维持负担得起的成本。虽然有许多点谱抽查框架,但利用统一测量仪对同一数据集上的这些作品进行一致比较仍然缺失。在本文件第一部分,我们提议将现有方法以基迪数据集作为基准。在第二部分,我们推出一个新的利达尔搜索模型:HALS:Height-Aware Lidar 超级分辨率。HALS利用了Lidar扫描显示高度-aware分布的观察,并采用了具有不同可接受域多个高度取样分支的发电机结构。HALS返回极地坐标,而不是球坐标,并使用地平标准损失。广泛的实验显示,HALS在3号实际数字上实现了状态。