Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 43 of its 73 references that are in the map. First posted on arXiv 1 Dec 2025.
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References · 43
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
- 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,234
- 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
- 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
- 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 740
- 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
- 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 616
- 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
- 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
31 more
- 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
- 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 377
- 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 335
- 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 259
- 2023GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfacescited by 252
- 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 217
- 2023Language Embedded 3D Gaussians for Open-Vocabulary Scene Understandingcited by 208
- 2021Neural Rays for Occlusion-aware Image-based Renderingcited by 192
- 2024PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generationcited by 191
- 20244D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenescited by 189
- 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 180
- 2023PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identificationcited by 154
- 2022SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fieldscited by 144
- 2021FCAF3D: Fully Convolutional Anchor-Free 3D Object Detectioncited by 97
- 2024Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussianscited by 97
- 2022Deforming Radiance Fields with Cagescited by 78
- 2025OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generationcited by 69
- 2023PIE-NeRF: Physics-based Interactive Elastodynamics with NeRFcited by 51
- 2024VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstructioncited by 50
- 2023Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attentioncited by 29
- 2023Neural Stress Fields for Reduced-order Elastoplasticity and Fracturecited by 24
- 2024MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Priorcited by 24
- 2024GASP: Gaussian Splatting for Physic-Based Simulationscited by 24
- 2025DepR: Depth Guided Single-view Scene Reconstruction with Instance-level Diffusioncited by 23
- 2024Bayesian Diffusion Models for 3D Shape Reconstructioncited by 19
- 2025VertexRegen: Mesh Generation with Continuous Level of Detailcited by 16
- 2020Cloth in the Wind: A Case Study of Physical Measurement through Simulationcited by 9
- 2024Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detectioncited by 6
- 2025C3Editor: Achieving Controllable Consistency in 2D Model for 3D Editingcited by 5
- 2024GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objectscited by 4
- 2024OPONeRF: One-Point-One NeRF for Robust Neural Renderingcited by 1
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