The recent advancements in 3D Gaussian splatting (3D-GS) have not only facilitated real-time rendering through modern GPU rasterization pipelines but have also attained state-of-the-art rendering quality. Nevertheless, despite its exceptional rendering quality and performance on standard datasets, 3D-GS frequently encounters difficulties in accurately modeling specular and anisotropic components. This issue stems from the limited ability of spherical harmonics (SH) to represent high-frequency information. To overcome this challenge, we introduce Spec-Gaussian, an approach that utilizes an anisotropic spherical Gaussian (ASG) appearance field instead of SH for modeling the view-dependent appearance of each 3D Gaussian. Additionally, we have developed a coarse-to-fine training strategy to improve learning efficiency and eliminate floaters caused by overfitting in real-world scenes. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. Thanks to ASG, we have significantly improved the ability of 3D-GS to model scenes with specular and anisotropic components without increasing the number of 3D Gaussians. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
最近在3D高斯喷溅（3D-GS）方面的进展，不仅通过现代GPU光栅化管道实现了实时渲染，而且还达到了最先进的渲染质量。然而，尽管在标准数据集上具有卓越的渲染质量和性能，3D-GS在准确建模镜面和各向异性成分方面经常遇到困难。这个问题源于球形谐波（SH）表示高频信息能力的限制。为了克服这个挑战，我们引入了Spec-Gaussian方法，该方法使用各向异性球形高斯（ASG）外观场而不是SH来建模每个3D高斯的视觉依赖外观。此外，我们还开发了一种从粗到细的训练策略，以提高学习效率并消除实际场景中过拟合造成的漂浮物。我们的实验结果表明，我们的方法在渲染质量方面超越了现有方法。多亏了ASG，我们显著提高了3D-GS建模具有镜面和各向异性成分场景的能力，而没有增加3D高斯的数量。这一改进扩展了3D GS处理具有镜面和各向异性表面的复杂场景的适用性。