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. Our project page is at this https URL

通过视觉观察来估计物理特性 (系统辨识) 的问题，本论文介绍了一种新颖的混合框架，该框架利用三维高斯表示不仅能捕捉明确的形状，还能使模拟连续推导训练中的隐含形状。我们提出了一种基于运动分解的新型动态三维高斯框架，以在不同时间状态下恢复物体作为三维高斯点集。此外，我们开发了一种由粗到细的填充策略，从高斯重建生成物体的密度场，允许提取物体连续及其表面，并将高斯属性融入这些连续体中。除提取的物体表面外，高斯信息的连续体还能在模拟中渲染物体掩膜，为物理特性估计提供隐含形状引导。大量实验评估表明，我们的管线在多个基准和度量标准上达到了最先进的性能。另外，我们通过真实世界演示展示了所提方法的实用性。