LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. As the performance of various LiDAR perception tasks has improved, generalizations to new environments and sensors has emerged to test these optimized models in real-world conditions. Unfortunately, the various annotation strategies of data providers complicate the computation of cross-domain performances. This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation to make it easier to evaluate the performance utilizing various source datasets. Alongside the dataset, online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided to ensure a fair comparison across methods. The ParisLuco3D dataset, evaluation scripts, and links to benchmarks can be found at the following website: https://npm3d.fr/parisluco3d

翻译：激光雷达通过收集场景的精确几何信息，是自动驾驶中不可或缺的传感器。随着各类激光雷达感知任务性能的提升，对新型环境和传感器的泛化能力已成为在实际条件下测试优化模型的关键挑战。然而，数据提供者不同的标注策略使跨域性能的计算复杂化。本文提出一个专为跨域评估设计的新型数据集——ParisLuco3D，旨在简化利用多样源数据集评估性能的流程。此外，我们提供了面向激光雷达语义分割、激光雷达目标检测和激光雷达跟踪的在线基准，以确保方法间的公平比较。ParisLuco3D数据集、评估脚本及基准链接可通过以下网址获取：https://npm3d.fr/parisluco3d