Texture-aware Intrinsic Image Decomposition with Model- and Learning-based Priors

• URL: http://arxiv.org/abs/2509.09352v1
• Date: Thu, 11 Sep 2025 11:07:25 GMT
• Title: Texture-aware Intrinsic Image Decomposition with Model- and Learning-based Priors
• Authors: Xiaodong Wang, Zijun He, Xin Yuan,
• Abstract summary: We propose a novel method for handling severe lighting and rich textures in intrinsic image decomposition.<n>We show that combining the novel texture-aware prior can produce superior results to existing approaches.
• Score: 10.34258784689083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper aims to recover the intrinsic reflectance layer and shading layer given a single image. Though this intrinsic image decomposition problem has been studied for decades, it remains a significant challenge in cases of complex scenes, i.e. spatially-varying lighting effect and rich textures. In this paper, we propose a novel method for handling severe lighting and rich textures in intrinsic image decomposition, which enables to produce high-quality intrinsic images for real-world images. Specifically, we observe that previous learning-based methods tend to produce texture-less and over-smoothing intrinsic images, which can be used to infer the lighting and texture information given a RGB image. In this way, we design a texture-guided regularization term and formulate the decomposition problem into an optimization framework, to separate the material textures and lighting effect. We demonstrate that combining the novel texture-aware prior can produce superior results to existing approaches.

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