DroneKey++: 一种无需尺寸先验的方法和新的无人机3D姿态估计基准
DroneKey++: A Size Prior-free Method and New Benchmark for Drone 3D Pose Estimation from Sequential Images
AI总结：
DroneKey++提出了一种无需先验信息的无人机3D姿态估计方法，并构建了大规模合成基准6DroneSyn，实现了高精度和高效率的姿态估计。
摘要：
准确的无人机3D姿态估计对于安全和监控系统至关重要。然而，现有方法往往依赖于先验的无人机信息，如物理尺寸或3D网格。同时，当前数据集规模较小，局限于单个模型，并在受限制的环境中收集，这使得可靠验证泛化能力变得困难。我们提出了DroneKey++，一种无需先验信息的框架，同时进行关键点检测、无人机分类和3D姿态估计。该框架采用关键点编码器同时进行关键点检测和分类，并采用基于射线的几何推理和类嵌入的姿态解码器来估计3D姿态。为了解决数据集限制，我们构建了6DroneSyn，一个大规模的合成基准，包含超过50000张图像，涵盖7种无人机模型和88种户外背景，通过360度全景合成生成。实验表明，DroneKey++在旋转方面达到MAE 17.34度和MedAE 17.1度，在平移方面达到MAE 0.135米和MedAE 0.242米，推理速度为19.25 FPS（CPU）和414.07 FPS（GPU），展示了在不同无人机模型上的强大泛化能力和适用于实时应用的适用性。该数据集已公开可用。
英文摘要：
Accurate 3D pose estimation of drones is essential for security and surveillance systems. However, existing methods often rely on prior drone information such as physical sizes or 3D meshes. At the same time, current datasets are small-scale, limited to single models, and collected under constrained environments, which makes reliable validation of generalization difficult. We present DroneKey++, a prior-free framework that jointly performs keypoint detection, drone classification, and 3D pose estimation. The framework employs a keypoint encoder for simultaneous keypoint detection and classification, and a pose decoder that estimates 3D pose using ray-based geometric reasoning and class embeddings. To address dataset limitations, we construct 6DroneSyn, a large-scale synthetic benchmark with over 50K images covering 7 drone models and 88 outdoor backgrounds, generated using 360-degree panoramic synthesis. Experiments show that DroneKey++ achieves MAE 17.34 deg and MedAE 17.1 deg for rotation, MAE 0.135 m and MedAE 0.242 m for translation, with inference speeds of 19.25 FPS (CPU) and 414.07 FPS (GPU), demonstrating both strong generalization across drone models and suitability for real-time applications. The dataset is publicly available.
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。
