LAA3D: A Benchmark of Detecting and Tracking Low-Altitude Aircraft in 3D Space
This provides a comprehensive benchmark for researchers working on low-altitude aerial vehicle perception, though it is incremental as it focuses on dataset creation rather than novel algorithmic breakthroughs.
The paper tackles the scarcity of datasets for 3D perception of low-altitude aircraft by introducing LAA3D, a large-scale dataset with 15,000 real images and 600,000 synthetic frames, which supports tasks like 3D detection and tracking, and demonstrates effective sim-to-real generalization with a proposed baseline method.
Perception of Low-Altitude Aircraft (LAA) in 3D space enables precise 3D object localization and behavior understanding. However, datasets tailored for 3D LAA perception remain scarce. To address this gap, we present LAA3D, a large-scale dataset designed to advance 3D detection and tracking of low-altitude aerial vehicles. LAA3D contains 15,000 real images and 600,000 synthetic frames, captured across diverse scenarios, including urban and suburban environments. It covers multiple aerial object categories, including electric Vertical Take-Off and Landing (eVTOL) aircraft, Micro Aerial Vehicles (MAVs), and Helicopters. Each instance is annotated with 3D bounding box, class label, and instance identity, supporting tasks such as 3D object detection, 3D multi-object tracking (MOT), and 6-DoF pose estimation. Besides, we establish the LAA3D Benchmark, integrating multiple tasks and methods with unified evaluation protocols for comparison. Furthermore, we propose MonoLAA, a monocular 3D detection baseline, achieving robust 3D localization from zoom cameras with varying focal lengths. Models pretrained on synthetic images transfer effectively to real-world data with fine-tuning, demonstrating strong sim-to-real generalization. Our LAA3D provides a comprehensive foundation for future research in low-altitude 3D object perception.