Gaussian Splatting in Mirrors: Reflection-Aware Rendering via Virtual Camera Optimization

TL;DR

This paper introduces a reflection-aware rendering method using virtual camera optimization in 3D Gaussian Splatting to improve multi-view mirror reflections, achieving high-quality, consistent results efficiently.

Contribution

It proposes modeling reflections as virtual cameras and optimizing their placement to enhance mirror rendering in 3D-GS, addressing previous limitations with a new virtual camera approach.

Findings

Improved multi-view mirror reflection quality.

Reduced training time compared to state-of-the-art.

Validated on real-world mirror datasets.

Abstract

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.