Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept

• URL: http://arxiv.org/abs/2505.19500v1
• Date: Mon, 26 May 2025 04:28:22 GMT
• Title: Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept
• Authors: Shogo Sato, Masaru Tsuchida, Mariko Yamaguchi, Takuhiro Kaneko, Kazuhiko Murasaki, Taiga Yoshida, Ryuichi Tanida,
• Abstract summary: Intrinsic image decomposition (IID) is the task of separating an image into albedo and shade.<n>In this paper, we propose a concept of quantitative evaluation with a calculated albedo from a hyperspectral imaging and light detection and ranging (LiDAR) intensity.<n>We also introduce an optional albedo densification approach based on spectral similarity.
• Score: 12.297351386142013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. )

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