Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
We propose a data-driven approach for intrinsic image decomposition, which is
the process of inferring the confounding factors of reflectance and shading in
an image. We pose this as a two-stage learning problem. First, we train a model
to predict relative reflectance ordering between image patches (brighter',
darker', `same') from large-scale human annotations, producing a data-driven
reflectance prior. Second, we show how to naturally integrate this learned
prior into existing energy minimization frameworks for intrinsic image
decomposition. We compare our method to the state-of-the-art approach of Bell
et al. on both decomposition and image relighting tasks, demonstrating the
benefits of the simple relative reflectance prior, especially for scenes under
challenging lighting conditions.
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Image RelightingIntrinsic Image DecompositionSimilar Papers 제목 키워드 기반
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