GaussianShader: 3D Gaussian Splatting with Shading Functions for
  Reflective Surfaces
Yingwenqi Jiang Jiadong Tu Yuan Liu Xifeng Gao Xiaoxiao Long Wenping Wang Yuexin Ma
The advent of neural 3D Gaussians has recently brought about a revolution in
the field of neural rendering, facilitating the generation of high-quality
renderings at real-time speeds. However, the explicit and discrete
representation encounters challenges when applied to scenes featuring
reflective surfaces. In this paper, we present GaussianShader, a novel method
that applies a simplified shading function on 3D Gaussians to enhance the
neural rendering in scenes with reflective surfaces while preserving the
training and rendering efficiency. The main challenge in applying the shading
function lies in the accurate normal estimation on discrete 3D Gaussians.
Specifically, we proposed a novel normal estimation framework based on the
shortest axis directions of 3D Gaussians with a delicately designed loss to
make the consistency between the normals and the geometries of Gaussian
spheres. Experiments show that GaussianShader strikes a commendable balance
between efficiency and visual quality. Our method surpasses Gaussian Splatting
in PSNR on specular object datasets, exhibiting an improvement of 1.57dB. When
compared to prior works handling reflective surfaces, such as Ref-NeRF, our
optimization time is significantly accelerated (23h vs. 0.58h). Please click on
our project website to see more results.