GASP: Gaussian Splatting for Physic-Based Simulations

• URL: http://arxiv.org/abs/2409.05819v1
• Date: Mon, 9 Sep 2024 17:28:57 GMT
• Title: GASP: Gaussian Splatting for Physic-Based Simulations
• Authors: Piotr Borycki, Weronika Smolak, Joanna Waczyńska, Marcin Mazur, Sławomir Tadeja, Przemysław Spurek,
• Abstract summary: Existing physics models use additional meshing mechanisms, including triangle or tetrahedron meshing, marching cubes, or cage meshes. We modify the physics grounded Newtonian dynamics to align with 3D Gaussian components. Resulting solution can be integrated into any physics engine that can be treated as a black box.
• Score: 0.42881773214459123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physics simulation is paramount for modeling and utilization of 3D scenes in various real-world applications. However, its integration with state-of-the-art 3D scene rendering techniques such as Gaussian Splatting (GS) remains challenging. Existing models use additional meshing mechanisms, including triangle or tetrahedron meshing, marching cubes, or cage meshes. As an alternative, we can modify the physics grounded Newtonian dynamics to align with 3D Gaussian components. Current models take the first-order approximation of a deformation map, which locally approximates the dynamics by linear transformations. In contrast, our Gaussian Splatting for Physics-Based Simulations (GASP) model uses such a map (without any modifications) and flat Gaussian distributions, which are parameterized by three points (mesh faces). Subsequently, each 3D point (mesh face node) is treated as a discrete entity within a 3D space. Consequently, the problem of modeling Gaussian components is reduced to working with 3D points. Additionally, the information on mesh faces can be used to incorporate further properties into the physics model, facilitating the use of triangles. Resulting solution can be integrated into any physics engine that can be treated as a black box. As demonstrated in our studies, the proposed model exhibits superior performance on a diverse range of benchmark datasets designed for 3D object rendering.

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