AeroDGS：面向单序列航拍4D重建的物理一致动态高斯泼溅
AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction
把可微的地面支撑、直立稳定与轨迹平滑先验注入 4D 高斯泼溅优化，为单目无人机视频中深度歧义与运动估计不稳的问题提供了可复用的物理约束思路；其单目几何提升模块也可迁移到其他单序列动态重建设定。
原文依据 a physics-guided 4D Gaussian splatting framework for monocular UAV videos
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期4D场景重建的进展大幅提升了各领域的动态建模能力。然而，现有方法在航拍条件下仍受限于单视角采集、大空间范围，以及空间占位有限但运动差异显著的动态物体。这些挑战导致严重的深度歧义与不稳定的运动估计，使单目航拍重建本质上是病态问题。为此，我们提出AeroDGS，一个面向单目无人机视频的物理引导4D高斯泼溅框架。AeroDGS引入单目几何提升模块，从单个航拍序列中重建可靠的静态与动态几何，为动态估计提供稳健基础。为进一步消解单目歧义，我们提出物理引导优化模块，融入可微的地面支撑、直立稳定与轨迹平滑先验，将模糊的图像线索转化为物理一致的运动。该框架以稳定的几何与连贯的时序演化联合细化静态背景与动态实体。我们还构建了一个真实无人机数据集，覆盖多种高度与运动条件，用于评测动态航拍重建。在合成与真实无人机场景上的实验表明，AeroDGS优于最优方法，在动态航拍环境中取得了更优的重建保真度。
英文原文摘要 1438 字符
Recent advances in 4D scene reconstruction have greatly 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 entities with stable geometry and coherent temporal evolution. We additionally build a real-world UAV dataset that spans various altitudes and motion conditions to evaluate dynamic aerial reconstruction. Experiments on synthetic and real UAV scenes demonstrate that AeroDGS outperforms state-of-the-art methods, achieving superior reconstruction fidelity in dynamic aerial environments.