mlsanityData to 13 Oct 2026

GASP: Gaussian Splatting for Physic-Based Simulations

arXiv 2409.05819 ↗First posted 9 Sep 2024cs.CVcited by 24

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Abstract (arXiv)

Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications. However, integrating 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. Alternatively, 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 GS for Physics-Based Simulations (GASP) pipeline uses parametrized flat Gaussian distributions. Consequently, the problem of modeling Gaussian components using the physics engine is reduced to working with 3D points. In our work, we present additional rules for manipulating Gaussians, demonstrating how to adapt the pipeline to incorporate meshes, control Gaussian sizes during simulations, and enhance simulation efficiency. This is achieved through the Gaussian grouping strategy, which implements hierarchical structuring and enables simulations to be performed exclusively on selected Gaussians. The resulting solution can be integrated into any physics engine that can be treated as a black box. As demonstrated in our studies, the proposed pipeline exhibits superior performance on a diverse range of benchmark datasets designed for 3D object rendering. The project webpage, which includes additional visualizations, can be found at https://waczjoan.github.io/GASP.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

15 of 27 references.

References · 15

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,170
  2. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,610
  3. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,462
  4. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,150
  5. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,070
  6. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 613
  7. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 570
  8. 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 370
  9. 2023SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenescited by 343
  10. 2024PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generationcited by 188
  11. 2024VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Realitycited by 177
  12. 2023DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splattingcited by 124
3 more
  1. 2024Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusioncited by 77
  2. 2024GaMeS: Mesh-Based Adapting and Modification of Gaussian Splattingcited by 70
  3. 2024D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soupcited by 18

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 12; 2026: 9.

24 papers.

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  1. 2025Generative Physical AI in Vision: A Surveycited by 24
  2. 2025A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generationcited by 19
  3. 2025DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interactioncited by 16
  4. 2024MiraGe: Editable 2D Images using Gaussian Splattingcited by 12
  5. 2025Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGCcited by 11
  6. 2025MASIV: Toward Material-Agnostic System Identification from Videoscited by 7
  7. 2026MOSIV: Multi-Object System Identification from Videoscited by 6
  8. 2024FruitNinja: 3D Object Interior Texture Generation with Gaussian Splattingcited by 5
  9. 2024GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objectscited by 4
  10. 2025Affine-Equivariant Kernel Space Encoding for NeRF Editingcited by 4
  11. 2025Gaussian-Augmented Physics Simulation and System Identification with Complex Colliderscited by 4
  12. 2026SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulationcited by 4
12 more
  1. 2026Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenescited by 3
  2. 2025PhysGaia: A Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesiscited by 2
  3. 2025GS-Verse: Mesh-based Gaussian Splatting for Physics-aware Interaction in Virtual Realitycited by 2
  4. 2026IRIS: Intersection-aware Ray-based Implicit Editable Scenescited by 2
  5. 2026HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactionscited by 2
  6. 2025REdiSplats: Ray Tracing for Editable Gaussian Splattingcited by 1
  7. 2025Generalized Dynamics Generation towards Scannable Physical World Modelcited by 0
  8. 2025Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussianscited by 0
  9. 2026VeloGauss: Learning Physically Consistent Gaussian Velocity Fields from Videoscited by 0
  10. 2026Real-Time Physics Simulation with Dynamic Mesh-Gaussian Reconstructionscited by 0
  11. 2026Scene-Level Heterogeneous Physics Simulation with 3D Gaussian Splatscited by 0
  12. 2026GaussPDE: Graph-Based Partial Differential Equation-Driven Rendering for 3D Gaussian Splattingcited by 0