用多阶段生成模型恢复烟雾模糊图像，提升3D重建质量。
GenSmoke-GS: A Multi-Stage Method for Novel View Synthesis from Smoke-Degraded Images Using a Generative Model
- 分步处理：先修复图像，再去雾，结合大模型增强，最后用3DGS-MCMC优化
- 在NTIRE 2026挑战赛中排名第一，相比基线视觉效果与量化指标均提升
- 适合做烟雾场景3D重建或图像恢复的研究者参考
本文介绍我们在NTIRE 2026 3D恢复与重建（3DRR）挑战赛第2赛道上针对烟雾退化图像的方法。烟雾会降低图像清晰度并破坏跨视图一致性，影响场景优化与渲染。我们提出多阶段流水线：图像修复、去雾、基于多模态大模型（MLLM）的增强、3DGS-MCMC优化以及多次运行结果平均。该流程旨在提升渲染前的可见性，同时最小化各输入视图间场景内容变化。在官方基准测试中，我们的方法在定量性能和视觉质量上均优于基线。代码已开源：https://github.com/plbbl/GenSmoke-GS。本方法在14名参赛者中排名首位，结果公布于：https://www.codabench.org/competitions/13993/#/results-tab。
原文摘要 · Abstract (English)
This paper describes our method for Track 2 of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge on smoke-degraded images. In this task, smoke reduces image visibility and weakens the cross-view consistency required by scene optimization and rendering. We address this problem with a multi-stage pipeline consisting of image restoration, dehazing, MLLM-based enhancement, 3DGS-MCMC optimization, and averaging over repeated runs. The main purpose of the pipeline is to improve visibility before rendering while limiting scene-content changes across input views. Experimental results on the challenge benchmark show improved quantitative performance and better visual quality than the provided baselines. The code is available at https://github.com/plbbl/GenSmoke-GS. Our method achieved a ranking of 1 out of 14 participants in Track 2 of the NTIRE 3DRR Challenge, as reported on the official competition website: https://www.codabench.org/competitions/13993/#/results-tab.
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