Problem
Research questions and friction points this paper is trying to address.
novel view synthesis
smoke-degraded images
cross-view consistency
image visibility
3D reconstruction
GenSmoke-GS: A Multi-Stage Method for Novel View Synthesis from Smoke-Degraded Images Using a Generative Model
🤖 AI Summary
This work addresses the degradation of image visibility and multi-view consistency caused by haze, which severely compromises novel view synthesis quality. To tackle this challenge, the authors propose a multi-stage optimization framework that sequentially integrates image restoration, dehazing, enhancement via multimodal large language models (MLLMs), and joint optimization of 3D Gaussian Splatting (3DGS) with Markov Chain Monte Carlo (MCMC), followed by averaging across multiple refinement rounds to simultaneously enhance visibility and preserve cross-view scene consistency. By synergistically combining generative priors with geometric optimization, the method achieved first place among 14 teams in Track 2 of the NTIRE 2026 3DRR Challenge, demonstrating superior quantitative performance and visual quality on the official benchmark.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
novel view synthesis
smoke-degraded image restoration
3D Gaussian Splatting
multi-stage pipeline
cross-view consistency
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