Abstract
3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view this http URL, 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.
Abstract (translated)
三维高斯点阵（3D Gaussian Splatting）在新颖视角渲染任务中展现了显著的能力，并且对于多视图立体视觉展示了巨大的潜力。然而，原始的3D高斯点阵在处理低光环境下的输入时缺乏颜色表示能力。简单地使用增强后的图像作为输入会导致多视图一致性问题，并且当前单视图增强系统依赖于预训练数据，这导致了场景泛化能力不足。这些问题限制了3D高斯点阵在包括高保真建模和特征匹配等领域的机器人技术中低光条件下的应用。 为了解决这些挑战，我们提出了一种基于高斯点阵的无监督多视图立体视觉系统，称为低光高斯点阵（Low-Light Gaussian Splatting, LLGS）。该系统旨在增强低光照环境中的图像并重建场景。我们的方法引入了一种可分解的高斯表示形式，称为M-Color，它可以单独表征颜色信息以进行有针对性的提升。此外，我们还提出了一种基于零先验知识的无监督优化方法，并使用方向导向性的增强技术来确保多视图一致性。 在真实世界数据集上的实验表明，我们的系统在低光环境下的图像增强以及三维高斯点阵任务中均优于现有的最先进技术。
URL
https://arxiv.org/abs/2503.18640
