提出可量化、绝对值且考虑色调的图像分解评估方法
Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept
- 用高光谱成像与激光雷达数据计算真实反照率作为评估基准
- 实验验证了该方法在实验室环境下可行，支持客观绝对评估
- 适合关注真实场景图像分解质量评估的研究者
内在图像分解（IID）旨在将图像分离为反照率和阴影。在真实场景中，由于缺乏真值，难以定量评估分解质量。现有方法依赖人工标注的相对反射强度，但存在主观性强、仅支持相对评价、无法评估色调等问题。为此，本文提出基于高光谱成像与激光雷达强度计算得到的真实反照率，实现定量评估；并引入基于光谱相似性的反照率补全方法。本研究在实验室环境中完成了概念验证，证明了客观、绝对且色度感知评估的可行性。（本文已被IEEE ICIP 2025接收）
原文摘要 · Abstract (English)
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. )
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。