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.

本文介绍了一种名为 GaussianShader 的新方法，通过在三维高斯函数上应用简化的着色函数，提升了在具有反射表面的场景中的神经渲染效果，并保持了训练和渲染的效率。通过基于三维高斯函数的最短轴方向设计了一种新颖的法线估计框架，并使用精心设计的损失函数使法线与高斯球的几何形状一致。实验证明，GaussianShader 在效率和视觉质量之间取得了可观的平衡，相较于处理反射表面的先前方法，如 Ref-NeRF，我们的方法显著加速了优化时间（23 小时对比 0.58 小时），在具有镜面对象数据集上的峰值信噪比方面也超过了 Gaussian Splatting，实现了 1.57dB 的提升。请点击访问我们的项目网站以查看更多结果。