LAA3D: A Benchmark of Detecting and Tracking Low-Altitude Aircraft in 3D Space
Hai Wu, Shuai Tang, Jiale Wang, Longkun Zou, Mingyue Guo, Rongqin Liang, Ke Chen, Yaowei Wang

TL;DR
LAA3D introduces a large-scale dataset and benchmark for 3D detection and tracking of low-altitude aircraft, facilitating advancements in aerial perception with diverse real and synthetic data.
Contribution
We present LAA3D, a comprehensive dataset and benchmark for low-altitude aircraft detection and tracking, including a monocular detection baseline and evaluation protocols.
Findings
Models pretrained on synthetic data transfer well to real-world scenarios.
MonoLAA achieves robust 3D localization from monocular images.
LAA3D enables standardized evaluation of low-altitude aircraft perception methods.
Abstract
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Robotics and Sensor-Based Localization · Advanced Vision and Imaging
