RGS: Reflection-aware Gaussian Splatting via Learning Geometry Continuity for Reflective Objects

2026-09-16

AI Summary

本文针对3D高斯点云方法在反射区域的表面塌陷问题，提出了一种基于物理的延迟渲染框架RGS，通过几何连续性学习和反射感知密集化策略来改善新视角合成的质量。

Abstract

Gaussian Splatting has significantly improved the quality of novel view synthesis with explicit Gaussian representation. However, we observed that existing 3D Gaussian Splatting methods (3DGS) often suffer from surface collapse issues on reflective regions, and thus produce inferior geometry and low-quality specular. In this work, we propose a physically-based deferred rendering framework, named Reflection-aware Gaussian Splatting (RGS), that can accurately model specular regions and improve novel view synthesis performance. Specifically, we found that a powerful 3D foundation model can provide a strong 3D geometric prior to foster correct geometric modeling. Based on this, we propose a cross-view shape consistency regularization to regularize the geometry surface with the large model prior and cross-view constraints. In this manner, our RGS can produce smoother geometric surfaces on reflective regions while reducing geometric hollows. To further improve rendering results on reflective regions, we present a reflection-aware densification strategy that is designed to capture specular variations across various views. With this strategy, our RGS is able to render novel views of objects in higher quality. Extensive experiments demonstrate our method consistently renders high-quality reflective objects, achieving state-of-the-art performance.

Problem

• Gaussian Splatting
• reflective regions
• surface collapse
• specular
• novel view synthesis

Innovation

• Reflection-aware Gaussian Splatting
• cross-view shape consistency regularization
• reflection-aware densification strategy