用可见光与热成像配对，解耦反射率与阴影，无需标注数据。
VT-Intrinsic: Physics-Based Decomposition of Reflectance and Shading using a Single Visible-Thermal Image Pair
- 基于物理规律，通过可见光与热成像强度关系推导反射率与阴影顺序。
- 在自然与人工光照下均优于现有物理与学习方法，定量指标领先。
- 适合做真实世界图像分解的自监督方案，尤其缺标注场景。
由于缺乏真实场景的丰富真值数据，将场景分解为反射率和阴影是一项挑战。本文提出一种基于物理的新方法，利用可见光与热成像图像对进行内在图像分解。其原理是：未被不透明表面反射的光线会被吸收并转化为热量，被热成像仪捕捉。因此，可见光与热成像的强度相对大小可对应于阴影与反射率的相对大小。这一相对关系为优化神经网络提供了密集的自监督信号，用于恢复阴影与反射率。我们在自然光与人工光条件下进行了定量评估，验证了已知反射率与阴影的表现，并在多种场景中开展定性实验。结果表明，该方法在性能上显著优于现有的物理方法与近期学习方法，为实现可扩展的真实世界数据标注提供了新路径。
原文摘要 · Abstract (English)
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.
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