GauSmoke: Hybrid Physics-Optical Gaussian Splatting for Sparse Smoke Reconstruction

摘要

Reconstructing dynamic fluids from sparse-view videos is a severely under-constrained problem due to complex volumetric visibility and turbulent, topology-changing motion. Current dynamic deformation-based 3D Gaussian Splatting (3DGS) methods use heuristic image-space warps, which often overfit limited observations via non-physical primitive scaling or drifting, producing floaters and geometric distortions. To address these challenges, we propose a hybrid, physics-aware fluid representation that injects physical constraints directly into the 3DGS pipeline. We develop a Beer–Lambert volumetric model mapping Gaussian primitives to a continuous, physically-grounded density field, bridging optical observations with fundamental physical laws. A physically-guided optimization evolves primitives under a global, volume-preserving velocity field, avoiding arbitrary per-particle deformations and effectively preserving volume while recovering fine-scale turbulent details. This unified formulation tightly couples fluid dynamics with differentiable rendering, enabling high-quality, joint predictions of velocity and density. Experiments on synthetic and real smoke datasets demonstrate that our approach surpasses state-of-the-art methods in both visual fidelity and physical consistency, achieving stable 3D reconstructions even from sparse views.