3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910155867684864 |
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| author | Zheng, Xinye Wang, Fei Nie, Yiqi Li, Kun Chen, Junjie Zhao, Jiaqi Wei, Yanyan Wu, Zhiliang |
| author_facet | Zheng, Xinye Wang, Fei Nie, Yiqi Li, Kun Chen, Junjie Zhao, Jiaqi Wei, Yanyan Wu, Zhiliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05687 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models Zheng, Xinye Wang, Fei Nie, Yiqi Li, Kun Chen, Junjie Zhao, Jiaqi Wei, Yanyan Wu, Zhiliang Computer Vision and Pattern Recognition 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. |
| title | 3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.05687 |