arXiv:2603.01491cs.CVcs.GR2026-03被引 1

提出新方法,让3D高斯点更准确还原材质与光照关系。

Radiometrically Consistent Gaussian Surfels for Inverse Rendering

  • 用物理渲染约束强制高斯点在未见视角下保持辐射一致性。
  • 在多个基准上超越现有方法,且重光照只需不到10毫秒。
  • 适合需要快速精准逆向渲染的场景,如影视特效、虚拟拍摄。

基于高斯点云的逆向渲染发展迅速,但准确分离材质属性与复杂全局光照(尤其是间接光照)仍是重大挑战。现有方法通常从为新视角合成训练好的高斯原型中查询间接辐射,但这些原型仅在有限视角下受监督,缺乏对未见视角间接辐射的建模能力。为此,我们提出辐射一致性这一新型物理约束,通过最小化每个高斯原型学习到的辐射与其物理渲染结果之间的残差,为未见视角提供监督。该机制在未见视角上建立自校正反馈环,同时融合物理渲染与新视角合成的监督信号,实现对多次反射的精确建模。我们进一步提出辐射一致性高斯面元(RadioGS),通过高效整合高斯面元与二维高斯射线追踪实现该原则。此外,我们设计了一种微调式重光照策略,可在数分钟内将高斯面元辐射适配至新光照条件,渲染成本低于10毫秒。在多个现有逆向渲染基准上的大量实验表明,RadioGS在逆向渲染性能上优于现有基于高斯的方法,同时保持计算效率。

原文摘要 · Abstract (English)

Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect illumination, remains a major challenge. Existing methods often query indirect radiance from Gaussian primitives pre-trained for novel-view synthesis. However, these pre-trained Gaussian primitives are supervised only towards limited training viewpoints, thus lack supervision for modeling indirect radiances from unobserved views. To address this issue, we introduce radiometric consistency, a novel physically-based constraint that provides supervision towards unobserved views by minimizing the residual between each Gaussian primitive's learned radiance and its physically-based rendered counterpart. Minimizing the residual for unobserved views establishes a self-correcting feedback loop that provides supervision from both physically-based rendering and novel-view synthesis, enabling accurate modeling of inter-reflection. We then propose Radiometrically Consistent Gaussian Surfels (RadioGS), an inverse rendering framework built upon our principle by efficiently integrating radiometric consistency by utilizing Gaussian surfels and 2D Gaussian ray tracing. We further propose a finetuning-based relighting strategy that adapts Gaussian surfel radiances to new illuminations within minutes, achieving low rendering cost (<10ms). Extensive experiments on existing inverse rendering benchmarks show that RadioGS outperforms existing Gaussian-based methods in inverse rendering, while retaining the computational efficiency.

逆向渲染高斯点云重光照物理渲染

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