TL;DR
This paper introduces SmokeGS-R, a physics-guided pipeline for multi-view smoke restoration that decouples geometry recovery from appearance correction, achieving improved reconstruction quality.
Contribution
The novel approach combines physics-based pseudo-clean supervision with a geometry-first reconstruction strategy and post-render appearance harmonization.
Findings
Achieved PSNR of 15.217 and SSIM of 0.666 on challenge test data.
Outperformed the official baseline by +3.68 dB PSNR on challenge scenes.
Code is publicly available at the provided GitHub repository.
Abstract
Real-world smoke simultaneously attenuates scene radiance, adds airlight, and destabilizes multi-view appearance consistency, making robust 3D reconstruction particularly difficult. We present \textbf{SmokeGS-R}, a practical pipeline developed for the NTIRE 2026 3D Restoration and Reconstruction Track 2 challenge. The key idea is to decouple geometry recovery from appearance correction: we generate physics-guided pseudo-clean supervision with a refined dark channel prior and guided filtering, train a sharp clean-only 3D Gaussian Splatting source model, and then harmonize its renderings with a donor ensemble using geometric-mean reference aggregation, LAB-space Reinhard transfer, and light Gaussian smoothing. On the official challenge testing leaderboard, the final submission achieved \mbox{PSNR } and \mbox{SSIM }. After the public release of RealX3D, we re-evaluated the…
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