Bird's-eye view (BEV) perception has garnered significant attention in autonomous driving in recent years, in part because BEV representation facilitates multi-modal sensor fusion. BEV representation enables a variety of perception tasks including BEV segmentation, a concise view of the environment useful for planning a vehicle's trajectory. However, this representation is not fully supported by existing datasets, and creation of new datasets for this purpose can be a time-consuming endeavor. To address this challenge, we introduce SimBEV. SimBEV is a randomized synthetic data generation tool that is extensively configurable and scalable, supports a wide array of sensors, incorporates information from multiple sources to capture accurate BEV ground truth, and enables a variety of perception tasks including BEV segmentation and 3D object detection. SimBEV is used to create the SimBEV dataset, a large collection of annotated perception data from diverse driving scenarios. SimBEV and the SimBEV dataset are open and available to the public.

翻译：鸟瞰图感知近年来在自动驾驶领域受到广泛关注，部分原因在于BEV表示有助于实现多模态传感器融合。BEV表示支持多种感知任务，包括BEV分割——一种对规划车辆轨迹有用的简明环境表示形式。然而，现有数据集未能完全支持这种表示形式，为此创建新数据集可能耗时费力。为应对这一挑战，我们提出了SimBEV。SimBEV是一种可随机生成的合成数据生成工具，具备高度可配置性和可扩展性，支持多种传感器类型，通过整合多源信息获取精确的BEV真值标注，并支持包括BEV分割和3D目标检测在内的多种感知任务。利用SimBEV工具构建的SimBEV数据集，收录了涵盖多样化驾驶场景的大规模标注感知数据。SimBEV工具及数据集已开源并向公众开放。