A framework for ultra high resolution 3D imaging

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We present an imaging framework to acquire 3D surface scans at ultra high-resolutions (exceeding 600 samples per mm). Our approach couples a standard structured-light setup and photometric stereo using a large-format ultrahigh-resolution camera. While previous approaches have employed similar hybrid imaging systems to fuse positional data with surface normals, what is unique to our approach is the significant asymmetry in the resolution between the low-resolution geometry and the ultra-high-resolution surface normals. To deal with these resolution differences, we propose a multi-resolution surface reconstruction scheme that propagates the low-resolution geometric constraints through the different frequency bands while gradually fusing in the high-resolution photometric stereo data. In addition, to deal with the ultra-high-resolution images, our surface reconstruction is performed in a patch-wise fashion and additional boundary constraints are used to ensure patch coherence. Based on this multi-resolution reconstruction scheme, our imaging framework can produce 3D scans that show exceptionally detailed 3D surfaces far exceeding existing technologies. ?2010 IEEE.
Publisher
IEEE
Issue Date
2010-06-13
Language
English
Citation

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp.1205 - 1212

ISSN
1063-6919
URI
http://hdl.handle.net/10203/167776
Appears in Collection
EE-Conference Papers(학술회의논문)
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