DeferredGS：基于延迟着色的解耦可编辑高斯泼溅
DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading
AI总结：
针对高斯泼溅中纹理与光照耦合及重光照伪影问题，提出DeferredGS方法，利用延迟着色、可学习环境贴图及蒸馏法线实现解耦，提升新视角合成与编辑效果。
摘要：
重建和编辑3D物体与场景在计算机图形学和计算机视觉中均扮演着至关重要的角色。神经辐射场（NeRFs）能够实现逼真的重建和编辑效果，但存在渲染效率低下的问题。高斯泼溅通过光栅化高斯椭球显著加速了渲染过程。然而，高斯泼溅利用单一的球谐（SH）函数同时建模纹理和光照，限制了对这些组件的独立编辑能力。近期，已有研究尝试使用高斯泼溅表示来解耦纹理和光照，但在反射场景上可能无法生成合理的几何形状和分解结果。此外，它们采用的向前着色技术在重新光照过程中会引入明显的混合伪影，因为高斯几何属性是在原始光照条件下优化的，可能不适用于新的光照条件。为解决这些问题，我们提出了DeferredGS，一种利用延迟着色对高斯泼溅表示进行解耦和编辑的方法。为实现成功的解耦，我们使用可学习的环境贴图对光照进行建模，并在高斯上定义额外的属性，如纹理参数和法线方向，其中法线是从联合训练的符号距离函数中蒸馏得到的。更重要的是，我们应用了延迟着色，与先前方法相比，产生了更逼真的重新光照效果。定性和定量实验均证明了DeferredGS在新视角合成和编辑任务中的优越性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
