DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred
  Shading
Tong Wu Jia-Mu Sun Yu-Kun Lai Yuewen Ma Leif Kobbelt Lin Gao
Reconstructing and editing 3D objects and scenes both play crucial roles in
computer graphics and computer vision. Neural radiance fields (NeRFs) can
achieve realistic reconstruction and editing results but suffer from
inefficiency in rendering. Gaussian splatting significantly accelerates
rendering by rasterizing Gaussian ellipsoids. However, Gaussian splatting
utilizes a single Spherical Harmonic (SH) function to model both texture and
lighting, limiting independent editing capabilities of these components.
Recently, attempts have been made to decouple texture and lighting with the
Gaussian splatting representation but may fail to produce plausible geometry
and decomposition results on reflective scenes. Additionally, the forward
shading technique they employ introduces noticeable blending artifacts during
relighting, as the geometry attributes of Gaussians are optimized under the
original illumination and may not be suitable for novel lighting conditions. To
address these issues, we introduce DeferredGS, a method for decoupling and
editing the Gaussian splatting representation using deferred shading. To
achieve successful decoupling, we model the illumination with a learnable
environment map and define additional attributes such as texture parameters and
normal direction on Gaussians, where the normal is distilled from a jointly
trained signed distance function. More importantly, we apply deferred shading,
resulting in more realistic relighting effects compared to previous methods.
Both qualitative and quantitative experiments demonstrate the superior
performance of DeferredGS in novel view synthesis and editing tasks.