mlsanityData to 14 Oct 2026

SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset

arXiv 2502.01894 ↗First posted 4 Feb 2025cs.CV cs.LG cs.ROcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 58 of its 78 references that are in the map. First posted on arXiv 4 Feb 2025.

Builds on

58 of 78 references.

References · 58

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  1. 2026SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perceptioncited by 0