Recent advancements in 3D Gaussian Splatting (3D-GS) have revolutionized novel view synthesis, facilitating real-time, high-quality image rendering. However, in scenarios involving reflective surfaces, particularly mirrors, 3D-GS often misinterprets reflections as virtual spaces, resulting in blurred and inconsistent multi-view rendering within mirrors. Our paper presents a novel method aimed at obtaining high-quality multi-view consistent reflection rendering by modelling reflections as physically-based virtual cameras. We estimate mirror planes with depth and normal estimates from 3D-GS and define virtual cameras that are placed symmetrically about the mirror plane. These virtual cameras are then used to explain mirror reflections in the scene. To address imperfections in mirror plane estimates, we propose a straightforward yet effective virtual camera optimization method to enhance reflection quality. We collect a new mirror dataset including three real-world scenarios for more diverse evaluation. Experimental validation on both Mirror-Nerf and our real-world dataset demonstrate the efficacy of our approach. We achieve comparable or superior results while significantly reducing training time compared to previous state-of-the-art.

翻译：三维高斯溅射（3D-GS）的最新进展革新了新视角合成技术，实现了实时高质量图像渲染。然而，在涉及反射表面（特别是镜子）的场景中，3D-GS常将反射误判为虚拟空间，导致镜内多视角渲染出现模糊与不一致现象。本文提出一种创新方法，通过将反射建模为基于物理的虚拟相机，以实现高质量的多视角一致反射渲染。我们利用3D-GS的深度与法向估计推算镜平面，并定义关于镜平面对称分布的虚拟相机。这些虚拟相机随后被用于解析场景中的镜面反射。针对镜平面估计的固有误差，我们提出一种简洁高效的虚拟相机优化方法以提升反射质量。我们构建了包含三种真实场景的新型镜面数据集以进行更全面的评估。在Mirror-Nerf数据集及我们采集的真实场景数据集上的实验验证表明，本方法在显著减少训练时间的同时，取得了与现有最优方法相当或更优的结果。