LO-Gaussian: Gaussian Splatting for Low-light and Overexposure Scenes through Simulated Filter
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Recent advancements in 3D Gaussian-based scene reconstruction and novel view synthesis have achieved impressive results. However, real-world images often suffer from adverse lighting conditions, which can hinder the performance of these techniques. Although progress has been made in addressing poor illumination, existing methods still struggle to accurately recover complex details in low-light and overexposed images. To address this challenge, we propose a method called LO-Gaussian, designed to recover illumination effectively in both low-light and overexposed scenes. Our approach involves simulating adverse lighting conditions during training, which is jointly optimized with the original 3D Gaussian rendering. During inference, the simulated filter is removed, allowing the model to decouple the scene under normal lighting conditions. We validate the effectiveness of our method through experiments on two publicly available datasets that include both poorly illuminated scenes and their corresponding normal illumination images. Experimental results demonstrate that LO-Gaussian consistently achieves optimal or near-optimal performance across these datasets, confirming the efficacy of our approach in illumination restoration.
Description
CCS Concepts: Computing methodologies → Computer vision representation; Rendering
@inproceedings{10.2312:pg.20241301,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{LO-Gaussian: Gaussian Splatting for Low-light and Overexposure Scenes through Simulated Filter}},
author = {You, Jingjiao and Zhang, Yuanyang and Zhou, Tianchen and Zhao, Yecheng and Yao, Li},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241301}
}