LO-Gaussian: Gaussian Splatting for Low-light and Overexposure Scenes through Simulated Filter

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dc.contributor.authorYou, Jingjiaoen_US
dc.contributor.authorZhang, Yuanyangen_US
dc.contributor.authorZhou, Tianchenen_US
dc.contributor.authorZhao, Yechengen_US
dc.contributor.authorYao, Lien_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:04:38Z
dc.date.available2024-10-13T18:04:38Z
dc.date.issued2024
dc.description.abstractRecent 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.en_US
dc.description.sectionheadersImage Processing and Filtering I
dc.description.seriesinformationPacific Graphics Conference Papers and Posters
dc.identifier.doi10.2312/pg.20241301
dc.identifier.isbn978-3-03868-250-9
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20241301
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20241301
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Computer vision representation; Rendering
dc.subjectComputing methodologies → Computer vision representation
dc.subjectRendering
dc.titleLO-Gaussian: Gaussian Splatting for Low-light and Overexposure Scenes through Simulated Filteren_US
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