SGS-Intrinsic：面向稀疏视角室内逆渲染的语义不变高斯泼溅
SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse Rendering
把语义与几何先验注入 3DGS，构建稠密且几何一致的高斯语义场，为稀疏视角下的室内逆渲染提供可靠几何基础；其光照不变材质约束与去阴影模型可直接复用于材质估计与重光照任务。
原文依据 construct a dense and geometry-consistent Gaussian semantic field guided by semantic and geometric priors
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 SGS-Intrinsic，一个在稀疏视角图像下仍能良好工作的室内逆渲染框架。现有基于三维高斯泼溅（3DGS）的方法多聚焦于以物体为中心的重建，且在稀疏视角设定下失效；与之不同，本方法能够实现高质量几何重建以及材质与光照的准确解耦。其核心思想是在语义与几何先验引导下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，本文结合混合光照模型与材质先验进行材质—光照解耦，以有效刻画光照与材质之间的相互作用。为缓解投射阴影的影响并提升材质恢复的鲁棒性，本文引入光照不变材质约束以及一个去阴影模型。在基准数据集上的大量实验表明，相较于现有基于 3DGS 的逆渲染方法，本方法在重建保真度与逆渲染质量上均取得一致提升。代码已公开于 https://github.com/GrumpySloths/SGS_Intrinsic.github.io。
英文原文摘要 1219 字符
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.