ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
This addresses the need for domain generalization in autonomous driving by offering a standardized dataset for testing LiDAR models across different environments and sensors, though it is incremental as it builds on existing data collection efforts.
The paper introduces ParisLuco3D, a dataset designed for cross-domain evaluation in LiDAR perception, providing benchmarks for semantic segmentation, object detection, and tracking to facilitate fair comparisons.
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