3D Gaussian Splatting for Imaged-Based Lighting

This paper presents a novel neural radiance field rendering method named 3D-IBLGS, which integrates prefiltered radiance fields to address global illumination in large-scale scenes. By extending the 3DGS formulation with image-space constraints, 3D-IBLGS optimizes implicit lighting distributions within neural images, capturing detailed spatial variations in lighting. The method efficiently renders general scenes while maintaining the quality of the original 3DGS and allows scene editing by changing materials or adding objects. Compared to Monte Carlo integration methods relying on environmental lighting, 3D-IBLGS demonstrates superior performance in decomposition and image synthesis, successfully segmenting the global illumination response of image components.