DocumentCode :
939145
Title :
Example-based photometric stereo: shape reconstruction with general, varying BRDFs
Author :
Hertzmann, Aaron ; Seitz, Steven M.
Author_Institution :
Dept. of Comput. Sci., Toronto Univ., Ont., Canada
Volume :
27
Issue :
8
fYear :
2005
Firstpage :
1254
Lastpage :
1264
Abstract :
This paper presents a technique for computing the geometry of objects with general reflectance properties from images. For surfaces with varying material properties, a full segmentation into different material types is also computed. It is assumed that the camera viewpoint is fixed, but the illumination varies over the input sequence. It is also assumed that one or more example objects with similar materials and known geometry are imaged under the same illumination conditions. Unlike most previous work in shape reconstruction, this technique can handle objects with arbitrary and spatially-varying BRDFs. Furthermore, the approach works for arbitrary distant and unknown lighting environments. Finally, almost no calibration is needed, making the approach exceptionally simple to apply.
Keywords :
image reconstruction; image segmentation; lighting; reflection; stereo image processing; bidirectional reflectance distribution function; clustering materials; illumination; photometric stereo; shape reconstruction; specular materials; Computational geometry; Image reconstruction; Image segmentation; Lighting; Material properties; Photometry; Reflectivity; Shape; Stereo image processing; Surface reconstruction; Index Terms- Photometric stereo; bidirectional reflectance distribution function (BRDF); clustering materials.; shape reconstruction; shape-from-shading; specular materials; Algorithms; Artificial Intelligence; Cluster Analysis; Colorimetry; Computer Simulation; Demography; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Models, Statistical; Pattern Recognition, Automated; Photogrammetry; Photometry;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2005.158
Filename :
1453513
Link To Document :
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