构建首个面向无人机的3D感知基准，支持单机与多机协同任务。
UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles
- 设计包含1000场景、每场景20帧的3D标注数据集
- 覆盖4类任务：单机/多机3D检测与追踪，车辆目标有完整3D框标注
- 解决传统2D基准无法支撑真实三维环境理解的问题
无人飞行器（UAV）搭载摄像头广泛应用于航拍、监控和农业等领域，其中鲁棒的目标检测与跟踪对有效部署至关重要。然而，现有无人机基准大多针对传统2D感知任务，限制了需三维环境理解的实际应用发展。此外，单架无人机视角有限，难以在远距离或遮挡区域保持有效感知。为此，我们提出UAV3D，一个面向无人机的3D感知基准，旨在推动单机与协同式3D感知研究。该基准包含1000个场景，每个场景含20帧图像，均带有车辆的完整3D边界框标注。提供四个3D感知任务：单机3D目标检测、单机目标跟踪、协同无人机3D目标检测、协同无人机目标跟踪。数据集与代码已开源：https://huiyegit.github.io/UAV3D_Benchmark/
原文摘要 · Abstract (English)
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/.
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