LiDAR is a sensor system that supports autonomous driving by gathering precise geometric information about the scene. Exploiting this information for perception is interesting as the amount of available data increases. As the quantitative performance of various perception tasks has improved, the focus has shifted from source-to-source perception to domain adaptation and domain generalization for perception. These new goals require access to a large variety of domains for evaluation. Unfortunately, the various annotation strategies of data providers complicate the computation of cross-domain performance based on the available data This paper provides a novel dataset, specifically designed for cross-domain evaluation to make it easier to evaluate the performance of various source datasets. Alongside the dataset, a flexible online benchmark is provided to ensure a fair comparison across methods.

翻译：激光雷达是一种通过采集场景精确几何信息来支撑自动驾驶的传感器系统。随着可用数据量的增加，利用此类信息进行感知具有重要意义。随着各类感知任务量化性能的提升，研究重点已从源域感知转向感知领域的域适应与域泛化。这些新目标要求获取大量不同域的数据用于评估。然而，数据提供方各异的标注策略给基于现有数据计算跨域性能带来了困难。本文提出一个专门为跨域评估设计的新型数据集，旨在简化不同源数据集性能的评估过程。伴随该数据集，我们提供了一套灵活的在线基准框架，以确保各方法间的公平比较。