ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception

Jules Sanchez, Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette

Abstract: 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.

External IDs:dblp:journals/ral/SanchezSDG24