GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces

Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang, Yuexin Ma; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 5322-5332

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).