基于语义先验的高斯点云，实现稀疏视角下的室内逆渲染。
SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse Rendering
- 构建语义引导的稠密高斯语义场，保证几何一致性。
- 融合光照-材质先验，精准分离材质与光照信息。
- 引入光照不变约束和去阴影模型，提升材质恢复鲁棒性。
我们提出 SGS-Intrinsic，一种适用于稀疏视角图像的室内逆渲染框架。不同于现有基于3D高斯溅射（3DGS）的方法仅在物体中心重建场景且在稀疏视角下表现不佳，本方法能实现高质量几何重建，并准确解耦材质与光照。核心思想是利用语义和几何先验构建稠密且几何一致的高斯语义场，为后续逆渲染提供可靠基础。在此基础上，结合混合光照模型与材质先验，有效建模光照-材质交互关系。为缓解投影阴影影响并增强材质恢复鲁棒性，引入光照不变材质约束及去阴影模型。在基准数据集上的大量实验表明，该方法在重建保真度和逆渲染质量上均优于现有3DGS基逆渲染方法。
原文摘要 · Abstract (English)
We present SGS-Intrinsic, an indoor inverse rendering framework that works well for sparse-view images. Unlike existing 3D Gaussian Splatting (3DGS) based methods that focus on object-centric reconstruction and fail to work under sparse view settings, our method allows to achieve high-quality geometry reconstruction and accurate disentanglement of material and illumination. The core idea is to construct a dense and geometry-consistent Gaussian semantic field guided by semantic and geometric priors, providing a reliable foundation for subsequent inverse rendering. Building upon this, we perform material-illumination disentanglement by combining a hybrid illumination model and material prior to effectively capture illumination-material interactions. To mitigate the impact of cast shadows and enhance the robustness of material recovery, we introduce illumination-invariant material constraint together with a deshadowing model. Extensive experiments on benchmark datasets show that our method consistently improves both reconstruction fidelity and inverse rendering quality over existing 3DGS-based inverse rendering approaches. Our code is available at https://github.com/GrumpySloths/SGS_Intrinsic.github.io.
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