Accurate 3D pose estimation of drones is essential for security and surveillance systems. However, existing datasets are small-scale, limited to single drone models, and collected under constrained environments, making reliable validation of generalization difficult. Since obtaining precise 3D drone pose annotations in real-world settings is nearly impossible due to safety regulations, equipment limitations, and cost constraints, synthetic data is the most practical path forward.

6DroneSyn is a comprehensive synthetic benchmark designed to overcome these limitations. It was generated using 360-degree panoramic synthesis with real-world background images, effectively minimizing the domain gap between synthetic and real data. Feature distribution analysis using PCA and t-SNE confirms that our synthetic data closely overlaps with real drone imagery, achieving both realism and diversity.

13 synthetic scenes featuring diverse drone motion patterns and 88 background variations from 22 real-world environments. Each scene generates independent sequences for each drone model.