Authors:
Masahiro Kitahara
1
;
Takahiro Okabe
1
;
Christian Fuchs
2
and
Hendrik P. A. Lensch
2
Affiliations:
1
Kyushu Institute of Technology, Japan
;
2
Tuebingen University, Germany
Keyword(s):
Multispectral Imaging, Photometric Stereo, Spectral Reflectance, Surface Normal.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Color and Texture Analyses
;
Computational Photography
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Pattern Recognition
;
Rendering
;
Software Engineering
Abstract:
Spectral reflectance is inherent characteristics of an object surface and therefore useful not only for computer
vision tasks such as material classification but also compute graphics applications such as relighting. In this
study, by integrating multispectral imaging and photometric stereo, we propose a method for simultaneously
estimating the spectral reflectance and normal per pixel from a small number of images taken under multispectral
and multidirectional light sources. In addition, taking attached shadows observed on curved surfaces into
consideration, we derive the minimum number of images required for the simultaneous estimation and propose
a method for selecting the optimal set of light sources. Through a number of experiments using real images,
we show that our proposed method can estimate spectral reflectances without the ambiguity of per-pixel scales
due to unknown normals, and that, when the optimal set of light sources is used, our method performs as well
as the straig
htforward method using a large number of images. Moreover, we demonstrated that estimating
both the spectral reflectances and normals is useful for relighting under novel illumination conditions.
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