ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
IEEE Robotics and Automation Letters(2023)
摘要
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
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关键词
Data Sets for Robotic Vision,Object Detection, Segmentation and Categorization,Intelligent Transportation Systems
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