Inherent limitations on specular highlight analysis

We analyse specular highlight modelling using microfacet-based physics illumination models. The ability to perform effective modelling is shown to depend on the ratio of the quantisation noise, ε , in the normal data to the object’s surface roughness parameter, m . We characterise how the accuracy d...

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Bibliographic Details
Published in:The Visual computer 2009-05, Vol.25 (5-7), p.647-656
Main Authors: Mac Manus, Lorcán, Iwasaki, Masahiro, Kanamori, Katsuhiro, Sato, Satoshi, Dodgson, Neil Anthony
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Graphics
Computer Science
Image Processing and Computer Vision
Least mean squares
Least mean squares algorithm
Modelling
Original Article
Surface roughness
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Summary:We analyse specular highlight modelling using microfacet-based physics illumination models. The ability to perform effective modelling is shown to depend on the ratio of the quantisation noise, ε , in the normal data to the object’s surface roughness parameter, m . We characterise how the accuracy degrades with increasing normal vector noise, when fitting is done in the Least Mean Squares sense. We show that it is not possible to accurately characterise sharp specular highlights, unless ε is very much less than m , and give examples of the practical implications of this theoretical result. We observe that the recently reported frequency-domain approaches can obscure this problem. We also present a novel characterisation of the importance of the geometric attenuation term in the microfacet models.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-009-0331-7