Interreflections in Computer Vision: A Survey and an Introduction to Spectral Infinite-Bounce Model

Deeb, Rada; Muselet, Damien; Hébert, Mathieu; Trémeau, Alain

doi:10.1007/s10851-017-0781-x

Cited by 11 publications

(19 citation statements)

References 36 publications

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“…So we need to use an extended version of this equation (defined for a single wavelength), in order to take into consideration all pixels and all wavelengths simultaneously. This version has been proposed in [3] as follow:…”

Section: The Interreflection Modelmentioning

confidence: 99%

“…Each image in the dataset corresponds to a V-shaped configuration of a surface with a homogeneous spectral reflectance. In order to do this, geometrical kernel values are obtained using Monte Carlo integration after choosing the angle, the sizes of the planar surfaces, and the discretization size as suggested in [3]. We consider a single collimated light source, parallel to the bisecting plane of the two panels, illuminating the V-cavities frontally.…”

Section: Self-interreflection Dataset Constructionmentioning

confidence: 99%

“…If one decides to fold the paper, the beams coming from the light source will bounce between the elementary surfaces thereby creating color variations across the paper (see Fig.1 and 4). This phenomenon is called interreflections and is well modeled by physics-based equations [1][2][3]. These models clearly explain that two elementary surfaces appear with different colors because they received different spectral lights.…”

Section: Introductionmentioning

confidence: 99%

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Deep spectral reflectance and illuminant estimation from self-interreflections

et al. 2018

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In this work, we propose a CNN-based approach to estimate the spectral reflectance of a surface and the spectral power distribution of the light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate the spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than the classical approaches. Our results show that the proposed approach outperforms the state of the art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.

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Section: The Interreflection Modelmentioning

confidence: 99%

Section: Self-interreflection Dataset Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep spectral reflectance and illuminant estimation from self-interreflections

et al. 2018

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“…In this section, we explain the used interreflection model for Lambertian surfaces [27], starting by the bases of this model and ending by its extension to a spectral form.…”

Section: Interreflection Modelmentioning

confidence: 99%

“…In this work, we first introduce the used interreflection model [27] which relates the variations of RGB values over a Lambertian concave surface to the reflectance of the surface, its shape, the illuminant and the used camera. Then, this model is used for spectral reflectance estimation applied in the case of a folded surface with a uniform spectral reflectance.…”

Section: Introductionmentioning

confidence: 99%

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

Deeb¹,

Muselet²,

Hébert³

et al. 2018

Appl. Opt.

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Light interreflections occurring in a concave object generate a color gradient that 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.

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Appearance synthesis of fluorescent objects with mutual illumination effects

Tominaga

Guarnera

2021

Color Research & Application

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We propose an approach for the appearance synthesis of objects with matte surfaces made of arbitrary fluorescent materials, accounting for mutual illumination. We solve the problem of rendering realistic scene appearances of objects placed close to each other under different conditions of uniform illumination, viewing direction, and shape, relying on standard physically based rendering and knowledge of the three‐dimensional shape and bispectral data of scene objects. The appearance synthesis model suggests that the overall appearance is decomposed into five components, each of which is expanded into a multiplication of spectral functions and shading terms. We show that only two shading terms are required, related to (a) diffuse reflection by direct illumination and (b) interreflection between two matte surfaces. The Mitsuba renderer is used to estimate the reflection components based on the underlying Monte Carlo simulation. The spectral computation of the fluorescent component is performed over a broad wavelength range, including ultraviolet and visible wavelengths. We also address a method for compensating for the difference between the simulated and real images. Experiments were performed to demonstrate the effectiveness of the proposed appearance synthesis approach. The accuracy of the proposed approach was experimentally confirmed using objects with different shapes and fluorescence in the presence of complex mutual illumination effects.

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Interreflections in Computer Vision: A Survey and an Introduction to Spectral Infinite-Bounce Model

Cited by 11 publications

References 36 publications

Deep spectral reflectance and illuminant estimation from self-interreflections

Deep spectral reflectance and illuminant estimation from self-interreflections

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

Appearance synthesis of fluorescent objects with mutual illumination effects

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