LumiGauss: Relightable Gaussian Splatting in the Wild

Decoupling lighting from geometry using unconstrained photo collections is notoriously challenging. Solving it would benefit many users as creating complex 3D assets takes days of manual labor. Many previous works have attempted to address this issue, often at the expense of output fidelity, which questions the practicality of such methods. We introduce LumiGauss—a technique that tackles 3D reconstruction of scenes and environmental lighting through 2D Gaussian Splatting. Our approach yields high-quality scene reconstructions and enables realistic lighting synthesis under novel environment maps. We also propose a method for enhancing the quality of shadows, common in outdoor scenes, by exploiting spherical harmonics properties. Our approach facilitates seamless integration with game engines and enables the use of fast precomputed radiance transfer. We validate our method on the NeRF-OSR dataset, demonstrating superior performance over baseline methods. Moreover, LumiGauss can synthesize realistic images for unseen environment maps.

1Warsaw University of Technology
2Sano Centre for Computational Medicine
3Microsoft
4IDEAS NCBR
5Tooploox

Qualitative Results

  • Novel View and Trained Light Synthesis
  • Point Light Synthesis
  • Sky Map
  • Other Scenes and Novel Light Synthesis

Bib Entry

            @inproceedings{kaleta2025lumigauss,
                title     = {{LumiGauss: Relightable Gaussian Splatting in the Wild}},
                author    = {
                    Kaleta, Joanna 
                    and Kania, Kacper 
                    and Trzci{\'n}ski, Tomasz
                    and Kowalski, Marek
                },
                booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
                year      = {2025}
            }