This paper studies the problem of estimating physical properties (system identification) through visual observations. To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework that leverages 3D Gaussian representation to not only capture explicit shapes but also enable the simulated continuum to deduce implicit shapes during training. We propose a new dynamic 3D Gaussian framework based on motion factorization to recover the object as 3D Gaussian point sets across different time states. Furthermore, we develop a coarse-to-fine filling strategy to generate the density fields of the object from the Gaussian reconstruction, allowing for the extraction of object continuums along with their surfaces and the integration of Gaussian attributes into these continuums. In addition to the extracted object surfaces, the Gaussian-informed continuum also enables the rendering of object masks during simulations, serving as implicit shape guidance for physical property estimation. Extensive experimental evaluations demonstrate that our pipeline achieves state-of-the-art performance across multiple benchmarks and metrics. Additionally, we illustrate the effectiveness of the proposed method through real-world demonstrations, showcasing its practical utility.
本文研究了通过视觉观察估计物理属性（系统识别）的问题。为了在物理属性估计中提供几何感知的引导，我们引入了一种新颖的混合框架，该框架利用3D高斯表征不仅捕捉显式形状，还在训练过程中使模拟的连续体推断隐式形状。我们提出了一个基于运动分解的新动态3D高斯框架，以在不同时间状态下恢复对象为3D高斯点集。此外，我们开发了一种由粗到细的填充策略，以从高斯重建生成对象的密度场，从而允许提取对象连续体及其表面，并将高斯属性整合到这些连续体中。除了提取的对象表面外，高斯信息的连续体还能在模拟中实现对象掩模的渲染，作为物理属性估计的隐式形状引导。广泛的实验评估表明，我们的流程在多个基准和指标上实现了最先进的性能。此外，我们通过现实世界的演示说明了所提方法的有效性，展示了其实际应用价值。