Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians

• URL: http://arxiv.org/abs/2512.01306v2
• Date: Sun, 07 Dec 2025 07:38:46 GMT
• Title: Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians
• Authors: Hongru Yan, Xiang Zhang, Zeyuan Chen, Fangyin Wei, Zhuowen Tu,
• Abstract summary: Gaussian Swaying is a surface-based framework for aerodynamic simulation using 3D Gaussians.<n>Our framework unifies simulation and rendering on the same representation: Gaussian patches.<n>Our framework achieves state-of-the-art performance and efficiency, offering a scalable approach for realistic aerodynamic scene simulation.
• Score: 35.72395384675807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Branches swaying in the breeze, flags rippling in the wind, and boats rocking on the water all show how aerodynamics shape natural motion -- an effect crucial for realism in vision and graphics. In this paper, we present Gaussian Swaying, a surface-based framework for aerodynamic simulation using 3D Gaussians. Unlike mesh-based methods that require costly meshing, or particle-based approaches that rely on discrete positional data, Gaussian Swaying models surfaces continuously with 3D Gaussians, enabling efficient and fine-grained aerodynamic interaction. Our framework unifies simulation and rendering on the same representation: Gaussian patches, which support force computation for dynamics while simultaneously providing normals for lightweight shading. Comprehensive experiments on both synthetic and real-world datasets across multiple metrics demonstrate that Gaussian Swaying achieves state-of-the-art performance and efficiency, offering a scalable approach for realistic aerodynamic scene simulation.

Related papers

• GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials [34.530526668727056]
  GaussianFluent is a framework for realistic simulation and rendering of dynamic object states.<n>It synthesizes interiors by densifying internal Gaussians guided by generative models.<n>It integrates an optimized Continuum Damage Material Point Method (CD-MPM) to enable brittle fracture simulation at remarkably high speed.
  arXiv  Detail & Related papers  (2026-01-14T07:59:52Z)
• ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer [11.17376076195671]
  ClimateGS is a novel framework integrating 3D Gaussian representations with physical simulation to enable real-time climate effects rendering.<n>We evaluate ClimateGS on MipNeRF360 and Tanks and Temples, demonstrating real-time rendering with comparable or superior visual quality to SOTA 2D/3D methods.
  arXiv  Detail & Related papers  (2025-03-19T03:01:35Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils [61.60175086194333]
  aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils.<n>Here, we consider modeling of incompressible flows over solid objects, wherein geometric structures are a key factor in determining aerodynamics.<n>To effectively incorporate geometries, we propose a message passing scheme that efficiently and expressively integrates the airfoil shape with the mesh representation.<n>These design choices lead to a purely data-driven machine learning framework known as GeoMPNN, which won the Best Student Submission award at the NeurIPS 2024 ML4CFD Competition, placing 4th overall.
  arXiv  Detail & Related papers  (2024-12-12T16:05:39Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation.<n>It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• GASP: Gaussian Splatting for Physic-Based Simulations [3.377471081417809]
  Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications.<n>Existing models use additional meshing mechanisms, including triangle or tetrahedron meshing, marching cubes, or cage meshes.<n>Alternatively, we can modify the physics-grounded Newtonian dynamics to align with 3D Gaussian components.<n>Our GS for Physics-Based Simulations (GASP) pipeline uses parametrized flat Gaussian distributions.
  arXiv  Detail & Related papers  (2024-09-09T17:28:57Z)
• GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation [60.33467489955188]
  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. We propose a new dynamic 3D Gaussian framework based on motion factorization to recover the object as 3D Gaussian point sets. In addition to the extracted object surfaces, the Gaussian-informed continuum also enables the rendering of object masks during simulations.
  arXiv  Detail & Related papers  (2024-06-21T07:37:17Z)
• MaGS: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting [27.081250446161114]
  This paper introduces the Mesh-adsorbed Gaussian Splatting (MaGS) method to address this challenge. MaGS constrains 3D Gaussians to roam near the mesh, creating a mutually adsorbed mesh-Gaussian 3D representation. Such representation harnesses both the rendering flexibility of 3D Gaussians and the structured property of meshes.
  arXiv  Detail & Related papers  (2024-06-03T17:59:51Z)
• Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)
• GaussianStyle: Gaussian Head Avatar via StyleGAN [64.85782838199427]
  We propose a novel framework that integrates the volumetric strengths of 3DGS with the powerful implicit representation of StyleGAN. We show that our method achieves state-of-the-art performance in reenactment, novel view synthesis, and animation.
  arXiv  Detail & Related papers  (2024-02-01T18:14:42Z)
• Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering [41.589093951039814]
  We integrate physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS. Our framework is capable of realistically reproducing surface highlights on dynamic fluids and facilitating interactions between scene objects and fluids from new views.
  arXiv  Detail & Related papers  (2024-01-27T06:45:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.