GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces
This work addresses a specific problem in neural rendering for reflective surfaces, offering an incremental improvement in efficiency and quality for applications like computer graphics and VR.
The paper tackles the challenge of applying neural 3D Gaussians to scenes with reflective surfaces by introducing GaussianShader, which uses a shading function and novel normal estimation to improve visual quality while maintaining efficiency, achieving a 1.57dB PSNR improvement over Gaussian Splatting and reducing optimization time from 23h to 0.58h compared to Ref-NeRF.
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.