Over the past few years, there has been remarkable progress in research on 3D point clouds and their use in autonomous driving scenarios has become widespread. However, deep learning methods heavily rely on annotated data and often face domain generalization issues. Unlike 2D images whose domains usually pertain to the texture information present in them, the features derived from a 3D point cloud are affected by the distribution of the points. The lack of a 3D domain adaptation benchmark leads to the common practice of training a model on one benchmark (e.g. Waymo) and then assessing it on another dataset (e.g. KITTI). This setting results in two distinct domain gaps: scenarios and sensors, making it difficult to analyze and evaluate the method accurately. To tackle this problem, this paper presents LiDAR Dataset with Cross Sensors (LiDAR-CS Dataset), which contains large-scale annotated LiDAR point cloud under six groups of different sensors but with the same corresponding scenarios, captured from hybrid realistic LiDAR simulator. To our knowledge, LiDAR-CS Dataset is the first dataset that addresses the sensor-related gaps in the domain of 3D object detection in real traffic. Furthermore, we evaluate and analyze the performance using various baseline detectors and demonstrated its potential applications. Project page: https://opendriving.github.io/lidar-cs.

翻译：近年来，三维点云研究取得了显著进展，其在自动驾驶场景中的应用已变得广泛。然而，深度学习方法严重依赖标注数据，且常面临领域泛化问题。与二维图像中领域通常涉及纹理信息不同，三维点云的特征受点云分布的影响。由于缺乏三维领域自适应基准，常见的做法是在一个基准（如Waymo）上训练模型，然后在另一个数据集（如KITTI）上进行评估。这种设置导致两种不同的领域差距：场景差距和传感器差距，使得难以准确分析和评估方法。为解决这一问题，本文提出了跨传感器激光雷达数据集（LiDAR-CS数据集），该数据集包含来自混合真实感激光雷达模拟器采集的六组不同传感器、但场景相同的海量标注激光雷达点云。据我们所知，LiDAR-CS数据集是首个针对真实交通场景中3D目标检测领域传感器相关差距的数据集。此外，我们利用多种基线检测器评估并分析了其性能，展示了该数据集的潜在应用。项目页面：https://opendriving.github.io/lidar-cs。