Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
arxiv(2024)
摘要
Reconstructing and simulating elastic objects from visual observations is
crucial for applications in computer vision and robotics. Existing methods,
such as 3D Gaussians, provide modeling for 3D appearance and geometry but lack
the ability to simulate physical properties or optimize parameters for
heterogeneous objects. We propose Spring-Gaus, a novel framework that
integrates 3D Gaussians with physics-based simulation for reconstructing and
simulating elastic objects from multi-view videos. Our method utilizes a 3D
Spring-Mass model, enabling the optimization of physical parameters at the
individual point level while decoupling the learning of physics and appearance.
This approach achieves great sample efficiency, enhances generalization, and
reduces sensitivity to the distribution of simulation particles. We evaluate
Spring-Gaus on both synthetic and real-world datasets, demonstrating accurate
reconstruction and simulation of elastic objects. This includes future
prediction and simulation under varying initial states and environmental
parameters. Project page: https://zlicheng.com/spring_gaus.
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