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LiDAR devices are widely used in autonomous driving scenarios and researcheson 3D point cloud achieve remarkable progress over the past years. However,deep learning-based methods heavily rely on the annotation data and often facethe domain generalization problem. Unlike 2D images whose domains are usuallyrelated to the texture information, the feature extracted from the 3D pointcloud is affected by the distribution of the points. Due to the lack of a 3Ddomain adaptation benchmark, the common practice is to train the model on onebenchmark (e.g, Waymo) and evaluate it on another dataset (e.g. KITTI).However, in this setting, there are two types of domain gaps, the scenariosdomain, and sensors domain, making the evaluation and analysis complicated anddifficult. To handle this situation, we propose LiDAR Dataset withCross-Sensors (LiDAR-CS Dataset), which contains large-scale annotated LiDARpoint cloud under 6 groups of different sensors but with same correspondingscenarios, captured from hybrid realistic LiDAR simulator. As far as we know,LiDAR-CS Dataset is the first dataset focused on the sensor (e.g., the pointsdistribution) domain gaps for 3D object detection in real traffic. Furthermore,we evaluate and analyze the performance with several baseline detectors on theLiDAR-CS benchmark and show its applications.

LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection