Abstract
Three-dimensional (3D) object detection based on point cloud data plays a critical role in the perception system of autonomous driving. However, this task presents a significant challenge in terms of its practical implementation due to the absence of point cloud data from automotive-grade hybrid solid-state LiDAR, as well as the limitations regarding the generalization ability of data-driven deep learning methods. In this paper, we introduce SimoSet, the first vehicle view 3D object detection dataset composed of automotive-grade hybrid solid-state LiDAR data. The dataset was collected from a university campus, contains 52 scenes, each of which are 8 s long, and provides three types of labels for typical traffic participants. We analyze the impact of the installation height and angle of the LiDAR on scanning effect and provide a reference process for the collection, annotation, and format conversion of LiDAR data. Finally, we provide baselines for LiDAR-only 3D object detection.
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