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

• URL: http://arxiv.org/abs/2604.05687v1
• Date: Tue, 07 Apr 2026 10:37:30 GMT
• Title: 3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models
• Abstract summary: Smoke introduces strong scattering effects, view-dependent appearance changes, and severe degradation of cross-view consistency.<n>We employ Nano-Banana-Pro to enhance smoke-degraded images and provide clearer visual observations for reconstruction.<n>We develop Smoke-GS, a medium-aware 3D Gaussian Splatting framework for smoke scene reconstruction.
• Score: 20.914336026776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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