GaRe: Relightable 3D Gaussian Splatting for Outdoor Scenes from Unconstrained Photo Collections
This work addresses the challenge of generating realistic and dynamic lighting effects for outdoor scenes, which is important for applications in computer graphics and virtual reality, though it appears incremental by building on existing 3D Gaussian splatting techniques.
The paper tackled the problem of outdoor scene relighting from unconstrained photo collections by proposing a 3D Gaussian splatting framework that integrates sunlight, sky radiance, and indirect lighting, resulting in competitive fidelity and more natural illumination and shadow effects compared to state-of-the-art methods.
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.