SIR：室内强光下具有可分解阴影的多视图逆向渲染
SIR: Multi-view Inverse Rendering with Decomposable Shadow Under Indoor Intense Lighting
AI总结：
提出SIR方法，利用多视图带位姿HDR图像和基于SDF的神经辐射场，通过学习可微阴影与三阶段材质估计，有效分解室内强光下的材质与光照，实现高质量逆向渲染及多种场景编辑功能。
摘要：
我们提出了SIR，一种利用多视图数据为室内场景逆向渲染分解可微阴影的高效方法，以解决准确分解材质和光照条件的挑战。与以往方法在复杂光照环境中难以保持阴影保真度不同，我们的方法显式地学习阴影，以增强在未知光照位置下材质估计的真实感。SIR使用带位姿的HDR图像作为输入，采用基于SDF的神经辐射场进行全面的场景表示。然后，SIR将阴影项与三阶段材质估计方法相结合，以提升SVBRDF质量。具体而言，SIR被设计为学习可微阴影，并辅以BRDF正则化，从而优化逆向渲染的准确性。在合成和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。SIR显著的分解能力使其具备复杂的编辑功能，如自由视角重照明、物体插入和材质替换。代码和数据可在https://xiaokangwei.github.io/SIR/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
