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[2602.22376] AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction
Summary
AeroDGS presents a novel framework for 4D reconstruction from monocular UAV videos, addressing challenges in depth ambiguity and motion estimation in dynamic aerial environments.
Why It Matters
This research is significant as it enhances the capabilities of aerial 4D reconstruction, which is crucial for applications in surveillance, mapping, and environmental monitoring. By improving the accuracy and stability of dynamic modeling from single-view captures, it opens new avenues for real-time data analysis in various fields.
Key Takeaways
- AeroDGS improves dynamic modeling in aerial 4D reconstruction.
- The framework uses a Monocular Geometry Lifting module for reliable geometry reconstruction.
- Physics-guided optimization resolves depth ambiguity and enhances motion consistency.
- A new UAV dataset was created to evaluate the framework's performance.
- AeroDGS outperforms existing methods in reconstruction fidelity.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22376 (cs) [Submitted on 25 Feb 2026] Title:AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction Authors:Hanyang Liu, Rongjun Qin View a PDF of the paper titled AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction, by Hanyang Liu and 1 other authors View PDF Abstract:Recent advances in 4D scene reconstruction have significantly improved dynamic modeling across various domains. However, existing approaches remain limited under aerial conditions with single-view capture, wide spatial range, and dynamic objects of limited spatial footprint and large motion disparity. These challenges cause severe depth ambiguity and unstable motion estimation, making monocular aerial reconstruction inherently ill-posed. To this end, we present AeroDGS, a physics-guided 4D Gaussian splatting framework for monocular UAV videos. AeroDGS introduces a Monocular Geometry Lifting module that reconstructs reliable static and dynamic geometry from a single aerial sequence, providing a robust basis for dynamic estimation. To further resolve monocular ambiguity, we propose a Physics-Guided Optimization module that incorporates differentiable ground-support, upright-stability, and trajectory-smoothness priors, transforming ambiguous image cues into physically consistent motion. The framework jointly refines static backgrounds and dynamic...