Piras, ValentinaBonatti, Amedeo FrancoMaria, Carmelo DeCignoni, PaoloBanterle, FrancescoCaputo, ArielGarro, ValeriaGiachetti, AndreaCastellani, UmbertoDulecha, Tinsae Gebrechristos2024-11-112024-11-112024978-3-03868-265-32617-4855https://doi.org/10.2312/stag.20241341https://diglib.eg.org/handle/10.2312/stag20241341The recent rise of Neural Radiance Fields (NeRFs)-like methods has revolutionized high-fidelity scene reconstruction, with 3D Gaussian Splatting (3DGS) standing out for its ability to generate photorealistic images while maintaining fast, efficient rendering. 3DGS delivers high-fidelity representations of complex scenes at any scale (from very small objects to entire cities), accurately capturing geometry, materials, and lighting, while meeting the need for fast and efficient rendering-crucial for applications requiring real-time performance. Although High Dynamic Range (HDR) technology, which enables the capture of comprehensive real-world lighting information, has been used in novel view synthesis, several questions remain unanswered. For example, does HDR improve the overall quality of reconstruction? Are 8 bits enough? Can tone mapped images be a balanced compromise regarding quality and details? To answer such questions, in this work, we study the application of HDR technology on the 3DGS method for acquiring real-world scenes.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Rendering; Computational photography; ReconstructionComputing methodologies → RenderingComputational photographyReconstruction10.2312/stag.2024134110 pages