VT-Intrinsic：基于单对可见光-热红外图像的物理反射率与光照分解
VT-Intrinsic: Physics-Based Decomposition of Reflectance and Shading using a Single Visible-Thermal Image Pair
利用可见光-热红外图像对的强度序关系进行自监督，为反射率与光照分解提供物理约束，可推广到其他需要物理先验的逆渲染任务。
原文依据 This allows us to relate the ordinalities (or relative magnitudes) between visible and thermal image intensities to the ordinalities of shading and reflectance.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论将场景分解为反射率和光照是一项挑战，因为真实世界场景缺乏大量真值数据。我们提出了一种新颖的基于物理的方法，利用一对可见光和热红外图像进行本征图像分解。我们利用的原理是：从不透明表面反射的光会被吸收并被热像仪检测为热量。这使我们能够将可见光和热红外图像强度之间的序关系（或相对大小）与光照和反射率的序关系联系起来。这些序关系使得优化神经网络能够进行密集自监督，以恢复光照和反射率。我们在自然光和人造光下使用已知的反射率和光照进行了定量评估，并在不同场景中进行了定性实验。结果表明，该方法优于基于物理的方法和近期基于学习的方法，为可扩展的真实世界数据监督管理提供了一条途径。
英文原文摘要 1004 字符
Decomposing a scene into its reflectance and shading is a challenge due to the lack of extensive ground-truth data for real-world scenes. We introduce a novel physics-based approach for intrinsic image decomposition using a pair of visible and thermal images. We leverage the principle that light not reflected from an opaque surface is absorbed and detected as heat by a thermal camera. This allows us to relate the ordinalities (or relative magnitudes) between visible and thermal image intensities to the ordinalities of shading and reflectance. The ordinalities enable dense self-supervision of an optimizing neural network to recover shading and reflectance. We perform quantitative evaluations with known reflectance and shading under natural and artificial lighting, and qualitative experiments across diverse scenes. The results demonstrate superior performance over both physics-based and recent learning-based methods, providing a path toward scalable real-world data curation with supervision.