OUR-GS: Online UAV Reconstruction with Adaptive Gaussian Mapping

Online UAV reconstruction provides timely 3D spatial understanding during flight, supporting perception and decision-making as UAVs operate in previously unseen environments. Recent aerial reconstruction methods increasingly build on 3D Gaussian Splatting for high-quality, large-scale reconstruction, yet remain predominantly offline and rely on pre-collected imagery and extensive scene coverage. The online setting is substantially more challenging for these methods, as reconstruction must proceed from incomplete observations over wide depth ranges and uneven multi-view evidence within a limited optimization budget. This work presents OUR-GS, an online monocular Gaussian reconstruction framework for UAV streams that adapts Gaussian map construction and refinement to viewing distance and available multi-view evidence. OUR-GS combines multi-view depth reassessment, balanced Gaussian map expansion, and tangential-radial decomposed position updates to enhance cross-frame depth consistency, representation across depth ranges, and online refinement, thereby improving online reconstruction quality. Experiments on five outdoor UAV datasets demonstrate that OUR-GS consistently improves reconstruction quality over existing methods, with particularly pronounced gains on forward-facing large-scale sequences, while maintaining the lowest GPU memory requirement. Ablation studies further validate the contribution of each component in OUR-GS. The code will be released.