Spectral reflectance estimation from one RGB image using self-interreflections in a concave object

TL;DR

This paper introduces a novel method to estimate spectral reflectance from a single RGB image of a concave object by leveraging self-interreflections, outperforming existing multi-image techniques.

Contribution

The paper presents a new approach using self-interreflections in a single image to estimate spectral reflectance, reducing the need for multiple images or calibration.

Findings

Single-image approach matches or exceeds multi-image methods.

Method outperforms existing approaches even without calibration.

Effective on real images under natural sunlight.

Abstract

Light interreflections occurring in a concave object generate a color gradient which is characteristic of the object's spectral reflectance. In this paper, we use this property in order to estimate the spectral reflectance of matte, uniformly colored, V-shaped surfaces from a single RGB image taken under directional lighting. First, simulations show that using one image of the concave object is equivalent to, and can even outperform, the state of the art approaches based on three images taken under three lightings with different colors. Experiments on real images of folded papers were performed under unmeasured direct sunlight. The results show that our interreflection-based approach outperforms existing approaches even when the latter are improved by a calibration step. The mathematical solution for the interreflection equation and the effect of surface parameters on the performance of the method are also discussed in this paper.