3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view
synthesis due to its detailed expressive ability and highly efficient rendering
speed. Unfortunately, creating relightable 3D assets with 3DGS is still
problematic, particularly for reflective objects, as its discontinuous
representation raises difficulties in constraining geometries. Inspired by
previous works, the signed distance field (SDF) can serve as an effective way
for geometry regularization. However, a direct incorporation between Gaussians
and SDF significantly slows training. To this end, we propose GS-ROR for
reflective objects relighting with 3DGS aided by SDF priors. At the core of our
method is the mutual supervision of the depth and normal between deferred
Gaussians and SDF, which avoids the expensive volume rendering of SDF. Thanks
to this mutual supervision, the learned deferred Gaussians are well-constrained
with a minimal time cost. As the Gaussians are rendered in a deferred shading
mode, while the alpha-blended Gaussians are smooth, individual Gaussians may
still be outliers, yielding floater artifacts. Therefore, we further introduce
an SDF-aware pruning strategy to remove Gaussian outliers, which are located
distant from the surface defined by SDF, avoiding the floater issue.
Consequently, our method outperforms the existing Gaussian-based inverse
rendering methods in terms of relighting quality. Our method also exhibits
competitive relighting quality compared to NeRF-based methods with at most 25%
of training time and allows rendering at 200+ frames per second on an RTX4090.

GS-ROR 是一种用于镜面反射物体照明调整的 3D 高斯喷洒方法，通过 SDF 先验的互相监督，提供了优于现有基于高斯的逆渲染方法的照明质量，同时具有最多 25% 的训练时间和每秒超过 200 帧的渲染速度。

GS-ROR: 通过 SDF 先验的 3D 高斯飞溅实现反射物体照明

GS-ROR: 3D Gaussian Splatting for Reflective Object Relighting via SDF  Priors

Decomposing a scene into its shape, reflectance, and illumination is a
challenging but important problem in computer vision and graphics. This problem
is inherently more challenging when the illumination is not a single light
source under laboratory conditions but is instead an unconstrained
environmental illumination. Though recent work has shown that implicit
representations can be used to model the radiance field of an object, most of
these techniques only enable view synthesis and not relighting. Additionally,
evaluating these radiance fields is resource and time-intensive. We propose a
neural reflectance decomposition (NeRD) technique that uses physically-based
rendering to decompose the scene into spatially varying BRDF material
properties. In contrast to existing techniques, our input images can be
captured under different illumination conditions. In addition, we also propose
techniques to convert the learned reflectance volume into a relightable
textured mesh enabling fast real-time rendering with novel illuminations. We
demonstrate the potential of the proposed approach with experiments on both
synthetic and real datasets, where we are able to obtain high-quality
relightable 3D assets from image collections. The datasets and code is
available on the project page: this https URL

本论文提出了一种使用基于物理的渲染来分解场景并产生具有 BRDF 材料属性的可重光纹理网格的方法，可适用于不同光照条件下获取的图像，实验证明该方法能够从图像集合中获得高质量的可重光 3D 资源。