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.

翻译：我们提出SIR，一种利用多视图数据对室内场景进行逆渲染的高效可分解可微阴影方法，旨在解决材料与光照条件精确分解的挑战。不同于现有方法在复杂光照环境下难以保证阴影保真度，我们的方法显式学习阴影以增强未知光源位置下的材质估计真实感。SIR以HDR位姿图像为输入，采用基于符号距离函数（SDF）的神经辐射场进行场景综合表征，随后通过阴影项与三阶段材质估计方法提升SVBRDF质量。具体而言，SIR通过设计可微阴影学习机制，辅以BRDF正则化，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在定量指标和定性分析上均优于现有方法。其显著的分解能力支持自由视点重光照、物体插入与材质替换等复杂编辑操作。