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arXiv · 2604.05687

3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models

Xinye Zheng·Fei Wang·Yiqi Nie·Kun Li·Junjie Chen·Jiaqi Zhao·Yanyan Wei·Zhiliang Wu
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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.

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Xinye Zheng, Fei Wang, Yiqi Nie, Kun Li, Junjie Chen, Jiaqi Zhao, Yanyan Wei, Zhiliang Wu. 2026-04-07. 3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models. https://arxiv.org/abs/2604.05687

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