低光环境下实现高保真3D重建，提升几何与光影一致性。
ELoG-GS: Dual-Branch Gaussian Splatting with Luminance-Guided Enhancement for Extreme Low-light 3D Reconstruction
- 双分支结构融合光照引导增强与点云学习初始化
- 实测在极端低光下达到PSNR 18.66、SSIM 0.6855
- 适合真实场景低光3D重建任务，代码开源可复现
本文针对NTIRE 2026 3D恢复与重建挑战赛（赛道1）提出方法，旨在从退化的多视角输入中重建高质量3D表示。挑战在于极端低光环境下恢复几何一致且逼真的3D场景。为此，我们提出极端低光优化高斯点阵（ELoG-GS），一种鲁棒的低光3D重建流程，结合基于学习的点云初始化与亮度引导的颜色增强，实现稳定且逼真的高斯点阵渲染。方法融合几何感知初始化与光照适应策略，在复杂条件下提升重建保真度。在NTIRE赛道1基准上的大量实验表明，相比基线方法，本方法显著提升重建质量，视觉保真度和几何一致性均更优。最终测试阶段，本方法在官方排行榜上取得PSNR 18.6626和SSIM 0.6855的成绩。代码已开源：https://github.com/lyh120/FSGS_EAPGS。
原文摘要 · Abstract (English)
This paper presents our approach to the NTIRE 2026 3D Restoration and Reconstruction Challenge (Track 1), which focuses on reconstructing high-quality 3D representations from degraded multi-view inputs. The challenge involves recovering geometrically consistent and photorealistic 3D scenes in extreme low-light environments. To address this task, we propose Extreme Low-light Optimized Gaussian Splatting (ELoG-GS), a robust low-light 3D reconstruction pipeline that integrates learning-based point cloud initialization and luminance-guided color enhancement for stable and photorealistic Gaussian Splatting. Our method incorporates both geometry-aware initialization and photometric adaptation strategies to improve reconstruction fidelity under challenging conditions. Extensive experiments on the NTIRE Track 1 benchmark demonstrate that our approach significantly improves reconstruction quality over the baselines, achieving superior visual fidelity and geometric consistency. The proposed method provides a practical solution for robust 3D reconstruction in real-world degraded scenarios. In the final testing phase, our method achieved a PSNR of 18.6626 and an SSIM of 0.6855 on the official platform leaderboard. Code is available at https://github.com/lyh120/FSGS_EAPGS.
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