LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction
- URL: http://arxiv.org/abs/2605.09299v1
- Date: Sun, 10 May 2026 03:45:50 GMT
- Title: LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction
- Abstract summary: Reconstructing 3D fluid velocity fields from sparse 2D video observations is a highly ill-posed inverse problem.<n>Existing methods typically impose these constraints through soft penalties, often leading to compromised accuracy and convergence issues.<n>We introduce a reconstruction framework that structurally enforces both constraints. Specifically, we parameterize the reconstructed velocity using a continuous Divergence-Free Kernel representation.
- Score: 26.888819976353957
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reconstructing 3D fluid velocity fields from sparse 2D video observations is a highly ill-posed inverse problem, demanding both transport consistency with observed motion and physical validity under fluid laws. Existing methods typically impose these constraints through soft penalties, often leading to compromised accuracy and convergence issues. We introduce a reconstruction framework that structurally enforces both constraints. Specifically, we parameterize the reconstructed velocity using a continuous Divergence-Free Kernel representation, driving the advection of a Lagrangian 3D Gaussian Splatting representation. This formulation intrinsically guarantees both flow incompressibility and long-range transport coherence by construction. To enable the efficient optimization of such a constrained system, we introduce a novel Sliding Window scheme that propagates gradients over meaningful temporal horizons while maintaining tractable training costs. Experiments on synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art baselines in both transport consistency and physical accuracy, enabling applications such as high-quality re-simulation and flow analysis.
Related papers
- Computational and Statistical Guarantees of the \ extit{c}-Rectified flow [4.787815288113813]
We study textitc-rectified flow, a cost-aware class of rectified flow that projects velocity fields onto a gradient class while preserving endpoint marginals.<n>We establish quantitative one-step contraction and exponential convergence guarantees under projection-stability assumptions for both quadratic and strongly convex displacement costs.<n>We show that, when combined with iterative textitc-rectified flow, they yield a rate-optimal estimator of the optimal transport for the dimension (d ge 3) and a nearly parametric rate for (d=1,2)
arXiv Detail & Related papers (2026-08-03T16:53:29Z) - Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency [74.90075313101933]
This paper revisits the same optical-flow primitive through a more local supervision design.<n>We use adjacent-frame Eulerian motion fields to guide generation, where the motion signal always describes a short temporal hop.<n>To mitigate the drift artifacts common in adjacent frame generation, we introduce a Bidirectional Geometric Consistency mechanism.
arXiv Detail & Related papers (2026-05-07T13:53:31Z) - A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics [4.010598744735379]
We introduce AegirJAX, a differentiable hydrodynamic solver based on the depth-integrated, non-static shallow-water equations.<n>AegirJAX treats the time-marching physics loop as a continuous computational graph.<n>We demonstrate the framework's versatility across a suite of scientific machine learning tasks.
arXiv Detail & Related papers (2026-04-08T14:21:47Z) - SSR: A Training-Free Approach for Streaming 3D Reconstruction [53.19807901585702]
Self-expressive Sequence Regularization (SSR) is a plug-and-play operator that enforces Grassmannian sequence regularity during inference.<n>We show that SSR consistently reduces drift and improves reconstruction quality across multiple streaming 3D reconstruction tasks.
arXiv Detail & Related papers (2026-03-16T02:55:14Z) - i-PhysGaussian: Implicit Physical Simulation for 3D Gaussian Splatting [60.46736489360263]
i-PhysGaussian is a framework that couples 3D Gaussian Splatting (3DGS) with an implicit Material Point Method (MPM) integrator.<n>Unlike explicit methods, our solution obtains an end-of-step state by minimizing a momentum-balance residual.<n>Results demonstrate that i-PhysGaussian maintains stability at up to 20x larger time steps than explicit baselines.
arXiv Detail & Related papers (2026-02-19T06:38:35Z) - FluxNet: Learning Capacity-Constrained Local Transport Operators for Conservative and Bounded PDE Surrogates [13.645285242786008]
We introduce a framework for learning conservative transport operators on regular grids.<n>Instead of predicting the next state, the model outputs local transport operators that update cells through neighborhood exchanges.<n>Experiments on shallow-water equations and traffic flow show improved rollout stability and physical consistency over strong baselines.
arXiv Detail & Related papers (2026-02-02T10:44:10Z) - On Exact Editing of Flow-Based Diffusion Models [97.0633397035926]
We propose Conditioned Velocity Correction (CVC) to reformulate flow-based editing as a distribution transformation problem driven by a known source prior.<n>CVC rethinks the role of velocity in inter-distribution transformation by introducing a dual-perspective velocity conversion mechanism.<n>We show that CVC consistently achieves superior fidelity, better semantic alignment, and more reliable editing behavior across diverse tasks.
arXiv Detail & Related papers (2025-12-30T06:29:20Z) - An Eulerian Perspective on Straight-Line Sampling [0.0]
We study dynamic measure transport for generative modeling, specifically, flows induced by processes that bridge a specified source and target distribution.<n>We ask emphwhich processes produce straight-line flows -- i.e., flows whose pointwise acceleration vanishes and thus are exactly integrable with a first-order method?<n>We provide a concise PDE characterization of straightness as a balance between conditional acceleration and the divergence of a weighted covariance (Reynolds) tensor.
arXiv Detail & Related papers (2025-10-13T17:33:58Z) - 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) - Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models [1.7810134788247751]
We present a method for reconstructing missing spatial and velocity data along the trajectories of small objects passively advected by turbulent flows.
Our approach makes use of conditional generative diffusion models, a recently proposed data-driven machine learning technique.
arXiv Detail & Related papers (2024-10-31T14:26:10Z) - Lagrangian Flow Networks for Conservation Laws [1.3385962626827215]
We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time.
LFlows show higher predictive accuracy in density modeling tasks compared to competing models in 2D and 3D.
As a real-world application, we model bird migration based on sparse weather radar measurements.
arXiv Detail & Related papers (2023-05-26T11:58:27Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z)
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.