ELoG-GS：双分支高斯点云生成与亮度引导增强用于极端低光3D重建
ELoG-GS: Dual-Branch Gaussian Splatting with Luminance-Guided Enhancement for Extreme Low-light 3D Reconstruction
AI总结：
本文提出ELoG-GS方法，结合学习点云初始化和亮度引导色彩增强，提升极端低光环境下3D重建的几何一致性和真实感。实验表明其在NTIRE 2026挑战赛中优于基线方法，达到更高的PSNR和SSIM。
摘要：
本文介绍了针对NTIRE 2026 3D修复与重建挑战（Track 1）的解决方案，旨在从退化的多视角输入中重建高质量的3D表示。挑战涉及在极端低光环境中恢复几何一致且逼真的3D场景。为此，我们提出了极端低光优化的高斯点云生成（ELoG-GS），一种稳健的低光3D重建管道，集成了基于学习的点云初始化和亮度引导的颜色增强，以实现稳定且逼真的高斯点云生成。我们的方法结合了几何感知的初始化和光度适应策略，以在挑战条件下提高重建保真度。在NTIRE Track 1基准上的广泛实验表明，我们的方法在重建质量上显著优于基线方法，实现了更优的视觉保真度和几何一致性。所提出的方法为现实世界退化场景中的稳健3D重建提供了实用解决方案。在最终测试阶段，我们的方法在官方平台排行榜上实现了PSNR为18.6626和SSIM为0.6855。代码可在https://github.com/lyh120/FSGS_EAPGS上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
