UAV3D：面向无人机的大规模三维感知基准
UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles
AI总结：
针对无人机三维感知与协同感知缺乏基准的问题，本文提出包含1000个场景的UAV3D基准，覆盖单机与协同的三维检测和跟踪四项任务，并公开数据集与代码。
摘要：
配备摄像头的无人机被广泛应用于航空摄影、监控和农业等众多领域。在这些应用中，稳健的目标检测与跟踪对于无人机的有效部署至关重要。然而，现有的无人机应用基准主要针对传统的二维感知任务设计，限制了需要三维环境理解的现实应用的发展。此外，尽管单无人机感知近年来取得了进展，但单一无人机平台有限的视野显著制约了其在远距离或遮挡区域中的感知能力。为解决这些挑战，我们提出了UAV3D，一个旨在推进无人机三维感知与协同三维感知任务研究的基准。UAV3D包含1,000个场景，每个场景包含20帧，并对车辆进行了完整标注的三维边界框。我们为四项三维感知任务提供基准：单无人机三维目标检测、单无人机目标跟踪、协同无人机三维目标检测和协同无人机目标跟踪。我们的数据集和代码可在 https://huiyegit.github.io/UAV3D_Benchmark/ 获取。
英文摘要：
Unmanned Aerial Vehicles (UAVs), equipped with cameras, are employed in numerous applications, including aerial photography, surveillance, and agriculture. In these applications, robust object detection and tracking are essential for the effective deployment of UAVs. However, existing benchmarks for UAV applications are mainly designed for traditional 2D perception tasks, restricting the development of real-world applications that require a 3D understanding of the environment. Furthermore, despite recent advancements in single-UAV perception, limited views of a single UAV platform significantly constrain its perception capabilities over long distances or in occluded areas. To address these challenges, we introduce UAV3D, a benchmark designed to advance research in both 3D and collaborative 3D perception tasks with UAVs. UAV3D comprises 1,000 scenes, each of which has 20 frames with fully annotated 3D bounding boxes on vehicles. We provide the benchmark for four 3D perception tasks: single-UAV 3D object detection, single-UAV object tracking, collaborative-UAV 3D object detection, and collaborative-UAV object tracking. Our dataset and code are available at https://huiyegit.github.io/UAV3D_Benchmark/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
