  LiDAR is an essential sensor for autonomous driving by collecting precise
geometric information regarding a scene. %Exploiting this information for
perception is interesting as the amount of available data increases. 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.
  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
