无监督提升暗光图像并重建3D场景，解决多视角一致性问题
LLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment
- 提出可分解的M-Color表示，分离颜色信息用于精准增强
- 无需先验知识的无监督优化，实现多视角一致性增强
- 适用于机器人领域暗光环境下的高保真建模与特征匹配
3D Gaussian Splatting在新视角渲染中表现优异，但在低光环境下缺乏颜色表征。直接使用增强图像会导致多视角不一致，现有单视图增强系统依赖预训练数据，泛化能力差。为解决这些问题，我们提出无监督多视图立体系统LLGS，旨在增强暗光图像的同时重建场景。方法引入可分解的M-Color表示，独立建模颜色信息以实现定向增强；并设计无监督优化策略，利用方向增强保证多视角一致性。在真实数据集上的实验表明，本方法在低光增强和3D Gaussian Splatting任务上均优于现有最先进方法。
原文摘要 · Abstract (English)
3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization.However, the original 3D Gaussian Splatting lacks color representation for inputs in low-light environments. Simply using enhanced images as inputs would lead to issues with multi-view consistency, and current single-view enhancement systems rely on pre-trained data, lacking scene generalization. These problems limit the application of 3D Gaussian Splatting in low-light conditions in the field of robotics, including high-fidelity modeling and feature matching. To address these challenges, we propose an unsupervised multi-view stereoscopic system based on Gaussian Splatting, called Low-Light Gaussian Splatting (LLGS). This system aims to enhance images in low-light environments while reconstructing the scene. Our method introduces a decomposable Gaussian representation called M-Color, which separately characterizes color information for targeted enhancement. Furthermore, we propose an unsupervised optimization method with zero-knowledge priors, using direction-based enhancement to ensure multi-view consistency. Experiments conducted on real-world datasets demonstrate that our system outperforms state-of-the-art methods in both low-light enhancement and 3D Gaussian Splatting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。