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

Abstract : Interreflections are observed on concave objects or when multiple objects are located closely. In a vision system, interreflections can largely affect color values captured by the camera. Due to this fact, mod-eling interreflections is important for many vision applications. In this paper, we consider the problem of treating and modeling interreflections in the domain of computer vision. First, a survey of existing approaches in the state of the art is given. These approaches are detailed, discussed and compared. Most of the state of the art models take into consideration only two bounces of light between surface elements. We, afterwards, introduce a new interreflection model based on radiomet-ric definitions. This model is the first one that takes into consideration an infinite number of light bounces between surface elements while providing image RGB values as a result. The accuracy of our model is studied by comparing it to real camera outputs. Thanks to our new model, the importance of using infinite bounces of light while studying interreflection, instead of only two bounces is demonstrated.
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Contributor : Damien Muselet <>
Submitted on : Monday, October 22, 2018 - 9:25:48 AM
Last modification on : Monday, January 13, 2020 - 5:46:07 PM
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  • HAL Id : hal-01900392, version 1


Rada Deeb, Damien Muselet, Mathieu Hébert, Alain Trémeau. Interreflections in Computer Vision: A Survey and an Introduction to Spectral Infinite-Bounce Model. Journal of Mathematical Imaging and Vision, Springer Verlag, 2018. ⟨hal-01900392⟩



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