发表机构
University of Technology Nuremberg; Intel; Max Planck Institute for Informatics, Saarland Informatics Campus（纽伦堡工业大学; 英特尔公司; 马克斯·普朗克信息研究所，萨尔兰信息园）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究逆渲染中材质-光照分解的不适定问题，提出结合物体跟踪、重建与逆渲染的方法，利用刚性运动物体的多样光-表面相互作用解决模糊性，实验证明该方法在合成数据和真实视频中均有优势。
AI 中文摘要
在逆渲染中将出射表面辐射分解为材质和光照，对于重光照和增强现实等应用至关重要，但由于多种组合可能导致相同的观测颜色，这一问题严重不适定。在多种光照条件下捕捉物体通常有助于解决这种模糊性，因为它将优化约束到正确的解决方案。在这项工作中，我们探索重建刚性运动物体（这提供了不同光-表面相互作用的观测）以解决逆渲染中材质-光照模糊性的潜力。为此，我们引入一种可重光照的方法，将物体跟踪和重建与一般刚性运动物体的逆渲染相结合。我们对合成数据的实验分析表明，运动对于分离材质和光照可能是一个优势：当物体在刚性运动下被观测时，重建的材质比静态时显著更准确。此外，对真实手持物体的RGB视频的结果表明，即使在有噪声的现实世界条件下，我们的流程也能保持这一优势。
英文摘要
Decomposing outgoing surface radiance into material and illumination during inverse rendering is essential for applications such as relighting and augmented reality, yet it is severely ill-posed since multiple combinations can result in the same observed colour. Capturing an object under multiple lighting conditions usually helps resolve this ambiguity as it constrains the optimization towards correct solutions. In this work, we explore the potential of reconstructing rigidly moving objects -- which provides observations of diverse light-surface interactions -- to resolve the material-lighting ambiguity in inverse rendering. For this purpose, we introduce a relightable approach that marries object tracking and reconstruction with inverse rendering for general rigidly moving objects. Our experimental analysis on synthetic data demonstrates that motion can be an advantage for disentangling material and lighting: the reconstructed material is significantly more accurate when the object is observed under rigid motion than when it is static. Moreover, results on RGB videos of real hand-held objects show that our pipeline preserves this advantage even under noisy real-world conditions.
CommentsAccepted at ECCV 2026. Project page: https://razayunus.github.io/DIR
