{"ID":2889482,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20512","arxiv_id":"2507.20512","title":"GaRe: Relightable 3D Gaussian Splatting for Outdoor Scenes from Unconstrained Photo Collections","abstract":"We propose a 3D Gaussian splatting-based framework for outdoor relighting that leverages intrinsic image decomposition to precisely integrate sunlight, sky radiance, and indirect lighting from unconstrained photo collections. Unlike prior methods that compress the per-image global illumination into a single latent vector, our approach enables simultaneously diverse shading manipulation and the generation of dynamic shadow effects. This is achieved through three key innovations: (1) a residual-based sun visibility extraction method to accurately separate direct sunlight effects, (2) a region-based supervision framework with a structural consistency loss for physically interpretable and coherent illumination decomposition, and (3) a ray-tracing-based technique for realistic shadow simulation. Extensive experiments demonstrate that our framework synthesizes novel views with competitive fidelity against state-of-the-art relighting solutions and produces more natural and multifaceted illumination and shadow effects.","short_abstract":"We propose a 3D Gaussian splatting-based framework for outdoor relighting that leverages intrinsic image decomposition to precisely integrate sunlight, sky radiance, and indirect lighting from unconstrained photo collections. Unlike prior methods that compress the per-image global illumination into a single latent vect...","url_abs":"https://arxiv.org/abs/2507.20512","url_pdf":"https://arxiv.org/pdf/2507.20512v1","authors":"[\"Haiyang Bai\",\"Jiaqi Zhu\",\"Songru Jiang\",\"Wei Huang\",\"Tao Lu\",\"Yuanqi Li\",\"Jie Guo\",\"Runze Fu\",\"Yanwen Guo\",\"Lijun Chen\"]","published":"2025-07-28T04:29:57Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}