用显式几何引导3D高斯点,实现更精准的材质光照解耦与渲染。
GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering
- 通过可优化网格构建带表面法向的3D高斯表示,提升光传输建模精度。
- 在多个数据集上实现领先逆渲染性能,材质分解更准确,光照重演更自然。
- 适合需要高保真材质光照分离的3D重建、影视特效与数字孪生场景。
近期的3D高斯溅射(3DGS)表示在新视角合成中表现优异;进一步地,对3DGS进行材质-光照解耦可赋予其光照重演能力并拓展应用范围。现有方法通常结合可微分物理基础渲染(PBR)技术联合恢复双向反射分布函数(BRDF)材质与环境光照,但精确解耦仍具挑战,主要因光传输建模困难。现有方法多近似高斯点的法向,构成隐式几何约束,但法向估计不准确会恶化光传输,导致材质分解噪声大、重演结果失真。为此,本文提出GeoSplatting,通过显式几何引导增强3DGS,实现精确光传输建模。我们通过可优化网格可微构建基于表面的3DGS,利用明确的网格法向与不透明网格表面,并支持基于网格的光线追踪技术,实现高效且考虑遮挡的光传输计算。该设计确保了精确的材质分解,同时保持3DGS的效率与高质量渲染能力。在多个数据集上的综合评估验证了方法的有效性,展现了卓越的效率与最先进的逆渲染性能。
原文摘要 · Abstract (English)
Recent 3D Gaussian Splatting (3DGS) representations have demonstrated remarkable performance in novel view synthesis; further, material-lighting disentanglement on 3DGS warrants relighting capabilities and its adaptability to broader applications. While the general approach to the latter operation lies in integrating differentiable physically-based rendering (PBR) techniques to jointly recover BRDF materials and environment lighting, achieving a precise disentanglement remains an inherently difficult task due to the challenge of accurately modeling light transport. Existing approaches typically approximate Gaussian points' normals, which constitute an implicit geometric constraint. However, they usually suffer from inaccuracies in normal estimation that subsequently degrade light transport, resulting in noisy material decomposition and flawed relighting results. To address this, we propose GeoSplatting, a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling. By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface, and additionally facilitates the use of mesh-based ray tracing techniques for efficient, occlusion-aware light transport calculations. This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS. Comprehensive evaluations across diverse datasets demonstrate the effectiveness of GeoSplatting, highlighting its superior efficiency and state-of-the-art inverse rendering performance. The project page can be found at https://pku-vcl-geometry.github.io/GeoSplatting/.
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