تقرير
Intrinsic Image Decomposition via Ordinal Shading
| العنوان: | Intrinsic Image Decomposition via Ordinal Shading |
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| المؤلفون: | Careaga, Chris, Aksoy, Yağız |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics |
| الوصف: | Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in various inverse rendering and computational photography pipelines. Generating highly accurate intrinsic decompositions is an inherently under-constrained task that requires precisely estimating continuous-valued shading and albedo. In this work, we achieve high-resolution intrinsic decomposition by breaking the problem into two parts. First, we present a dense ordinal shading formulation using a shift- and scale-invariant loss in order to estimate ordinal shading cues without restricting the predictions to obey the intrinsic model. We then combine low- and high-resolution ordinal estimations using a second network to generate a shading estimate with both global coherency and local details. We encourage the model to learn an accurate decomposition by computing losses on the estimated shading as well as the albedo implied by the intrinsic model. We develop a straightforward method for generating dense pseudo ground truth using our model's predictions and multi-illumination data, enabling generalization to in-the-wild imagery. We present an exhaustive qualitative and quantitative analysis of our predicted intrinsic components against state-of-the-art methods. Finally, we demonstrate the real-world applicability of our estimations by performing otherwise difficult editing tasks such as recoloring and relighting. Comment: 24 pages, 23 figures, Accepted to ACM Transactions on Graphics (2023). Project page: https://yaksoy.github.io/intrinsic/ |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3630750 |
| URL الوصول: | http://arxiv.org/abs/2311.12792 |
| رقم الأكسشن: | edsarx.2311.12792 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3630750 |
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