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 when applying novel environment maps.
从不受约束的照片集群中分离光照和几何信息是一个众所周知的挑战。解决这一问题将为许多用户带来巨大益处，因为创建复杂的3D资产通常需要耗费数天的手工劳动。许多之前的研究试图解决这个问题，但往往以牺牲输出保真度为代价，进而质疑这些方法的实用性。 我们引入了LumiGauss，这是一种通过2D高斯点绘技术处理场景和环境光照3D重建的技术。我们的方法能够产生高质量的场景重建，并在新的环境贴图下实现逼真的光照合成。此外，我们提出了一种通过利用球谐函数特性来增强户外场景中常见阴影质量的方法。我们的方案不仅能与游戏引擎无缝集成，还支持使用快速预计算辐射传输。 我们在NeRF-OSR数据集上验证了我们的方法，展示了其相对于基线方法的优越性能。此外，LumiGauss在应用新的环境贴图时能够合成逼真的图像。