DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading
This addresses the need for efficient and editable 3D representations in computer graphics and vision, though it is incremental as it builds on existing Gaussian splatting methods.
The paper tackles the problem of decoupling texture and lighting in Gaussian splatting for 3D reconstruction and editing, which previous methods struggled with on reflective scenes and introduced blending artifacts during relighting. The result is DeferredGS, which uses deferred shading and additional attributes to achieve more realistic relighting effects, as shown by superior performance in novel view synthesis and editing tasks in experiments.
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.