Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909623055810560 |
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| author | Sato, Shogo Tsuchida, Masaru Yamaguchi, Mariko Kaneko, Takuhiro Murasaki, Kazuhiko Yoshida, Taiga Tanida, Ryuichi |
| author_facet | Sato, Shogo Tsuchida, Masaru Yamaguchi, Mariko Kaneko, Takuhiro Murasaki, Kazuhiko Yoshida, Taiga Tanida, Ryuichi |
| contents | Intrinsic image decomposition (IID) is the task of separating an image into albedo and shade. In real-world scenes, it is difficult to quantitatively assess IID quality due to the unavailability of ground truth. The existing method provides the relative reflection intensities based on human-judged annotations. However, these annotations have challenges in subjectivity, relative evaluation, and hue non-assessment. To address these, we propose a concept of quantitative evaluation with a calculated albedo from a hyperspectral imaging and light detection and ranging (LiDAR) intensity. Additionally, we introduce an optional albedo densification approach based on spectral similarity. This paper conducted a concept verification in a laboratory environment, and suggested the feasibility of an objective, absolute, and hue-aware assessment. (This paper is accepted by IEEE ICIP 2025. ) |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_19500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept Sato, Shogo Tsuchida, Masaru Yamaguchi, Mariko Kaneko, Takuhiro Murasaki, Kazuhiko Yoshida, Taiga Tanida, Ryuichi Computer Vision and Pattern Recognition Intrinsic image decomposition (IID) is the task of separating an image into albedo and shade. In real-world scenes, it is difficult to quantitatively assess IID quality due to the unavailability of ground truth. The existing method provides the relative reflection intensities based on human-judged annotations. However, these annotations have challenges in subjectivity, relative evaluation, and hue non-assessment. To address these, we propose a concept of quantitative evaluation with a calculated albedo from a hyperspectral imaging and light detection and ranging (LiDAR) intensity. Additionally, we introduce an optional albedo densification approach based on spectral similarity. This paper conducted a concept verification in a laboratory environment, and suggested the feasibility of an objective, absolute, and hue-aware assessment. (This paper is accepted by IEEE ICIP 2025. ) |
| title | Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.19500 |