AeroGS: Scale-Aware Gaussian Splatting for Pose-Free Dynamic UAV Scene Reconstruction

Tingyun Li, Xinyi Liu, Yongjun Zhang, Yi Wan, Xiaoan Liu, Weiwei Fan, Jiahao Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 40907-40917

Monocular UAV videos pose a fundamental challenge for 3D reconstruction: dynamic scene modeling requires accurate camera poses, yet recovering poses from long UAV trajectories often fails in texture-sparse regions and in the presence of moving objects. Existing approaches typically handle either pose-free static reconstruction or dynamic reconstruction with known poses, but jointly solving both from casual aerial footage remains difficult due to motion coupling and severe scale variation. We introduce AeroGS, a scale-aware Gaussian splatting framework that jointly recovers camera trajectories and reconstructs dynamic scenes from pose-free monocular videos. Central to our method are scale-aware spatio-temporal anchors (S2A-Anchors), which enable a unified optimization via three key decoupling mechanisms: (i) separating ego-motion from object motion, (ii) isolating static geometry from temporal deformation, and (iii) adapting to scale variation between distant terrain and nearby objects. This design effectively stabilizes optimization under large motion and scale imbalance. Extensive experiments on UAV and driving benchmarks show that AeroGS achieves state-of-the-art rendering quality (PSNR/LPIPS), precise trajectory recovery (ATE/RPE), and faithful motion reconstruction, consistently surpassing recent pose-free baselines.