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).
神经3D高斯的出现最近在神经渲染领域引发了一场革命，促进了以实时速度生成高质量渲染的能力。然而，这种明确和离散的表示在应用于具有反射表面的场景时遇到了挑战。在这篇论文中，我们介绍了GaussianShader，这是一种新方法，它在3D高斯上应用了简化的着色函数，以增强具有反射表面的场景中的神经渲染，同时保持训练和渲染效率。应用着色函数的主要挑战在于对离散3D高斯上的精确法线估计。具体来说，我们提出了一种基于3D高斯最短轴方向的新颖法线估计框架，并设计了一个精细的损失函数，以确保法线与高斯球的几何形状之间的一致性。实验表明，GaussianShader在效率和视觉质量之间取得了值得称赞的平衡。我们的方法在反射物体数据集上的PSNR上超过了高斯喷溅，展示了1.57dB的改进。与处理反射表面的先前工作（如Ref-NeRF）相比，我们的优化时间显著加快（23小时对比0.58小时）。