We propose SIR, an efficient method to decompose differentiable shadows for inverse rendering on indoor scenes using multi-view data, addressing the challenges in accurately decomposing the materials and lighting conditions. Unlike previous methods that struggle with shadow fidelity in complex lighting environments, our approach explicitly learns shadows for enhanced realism in material estimation under unknown light positions. Utilizing posed HDR images as input, SIR employs an SDF-based neural radiance field for comprehensive scene representation. Then, SIR integrates a shadow term with a three-stage material estimation approach to improve SVBRDF quality. Specifically, SIR is designed to learn a differentiable shadow, complemented by BRDF regularization, to optimize inverse rendering accuracy. Extensive experiments on both synthetic and real-world indoor scenes demonstrate the superior performance of SIR over existing methods in both quantitative metrics and qualitative analysis. The significant decomposing ability of SIR enables sophisticated editing capabilities like free-view relighting, object insertion, and material replacement. The code and data are available at https://xiaokangwei.github.io/SIR/.

翻译：我们提出SIR方法，这是一种利用多视角数据对室内场景进行逆渲染的高效可微分阴影分解方法，旨在解决材料与光照条件精确分解的挑战。不同于既往方法在复杂光照环境中难以保持阴影保真度的问题，本文方法在未知光源位置下通过显式学习阴影来增强材料估计的真实感。SIR以高动态范围图像为输入，采用基于有符号距离函数的神经辐射场实现场景综合表征。在此基础上，通过融合阴影项与三阶段材料估计策略，提升空间变化双向反射分布函数的质量。具体而言，SIR被设计为学习可微分阴影并辅以双向反射分布函数正则化，从而优化逆渲染精度。在合成与真实室内场景上的大量实验表明，SIR在定量指标与定性分析中均显著优于现有方法。其强大的分解能力进一步支持自由视角重光照、物体插入及材料替换等复杂编辑操作。相关代码与数据可从https://xiaokangwei.github.io/SIR/获取。