Abstract
Reconstructing 3D scenes from smoke-degraded multi-view images is particularly difficult because smoke introduces strong scattering effects, view-dependent appearance changes, and severe degradation of cross-view consistency. To address these issues, we propose a framework that integrates visual priors with efficient 3D scene modeling. We employ Nano-Banana-Pro to enhance smoke-degraded images and provide clearer visual observations for reconstruction and develop Smoke-GS, a medium-aware 3D Gaussian Splatting framework for smoke scene reconstruction and restoration-oriented novel view synthesis. Smoke-GS models the scene using explicit 3D Gaussians and introduces a lightweight view-dependent medium branch to capture direction-dependent appearance variations caused by smoke. Our method preserves the rendering efficiency of 3D Gaussian Splatting while improving robustness to smoke-induced degradation. Results demonstrate the effectiveness of our method for generating consistent and visually clear novel views in challenging smoke environments.
Abstract (translated)
从烟雾退化的多视角图像重建三维场景尤为困难，因为烟雾会引入强烈的散射效应、视角相关的外观变化以及跨视角一致性的严重破坏。针对这些问题，我们提出了一种整合视觉先验与高效三维场景建模的框架。我们采用Nano-Banana-Pro增强烟雾退化图像，为重建提供更清晰的视觉观测，并开发了Smoke-GS——一种面向烟雾场景重建与修复导向的新视角合成的介质感知三维高斯泼溅框架。Smoke-GS利用显式三维高斯元建模场景，并引入轻量级的视角相关介质分支，以捕捉烟雾导致的视角相关外观变化。我们的方法在保持三维高斯泼溅渲染效率的同时，提升了对烟雾退化影响的鲁棒性。结果表明，该方法在具有挑战性的烟雾环境中生成一致且视觉清晰的新视角方面具有有效性。
URL
https://arxiv.org/abs/2604.05687
