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

TL;DR

This paper introduces a novel quantitative, objective, and hue-aware evaluation method for intrinsic image decomposition in real-world scenes, addressing limitations of subjective human annotations.

Contribution

It proposes a new evaluation framework using hyperspectral and LiDAR data, along with an optional albedo densification approach, for more accurate IID assessment.

Findings

Feasibility demonstrated in laboratory environment

Objective and hue-aware metrics successfully assessed IID quality

Potential for improved real-world scene analysis

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. )