Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2010.5539829
Title: A framework for ultra high resolution 3D imaging
Authors: Lu, Z. 
Tai, Y.-W.
Ben-Ezra, M.
Brown, M.S. 
Issue Date: 2010
Citation: Lu, Z., Tai, Y.-W., Ben-Ezra, M., Brown, M.S. (2010). A framework for ultra high resolution 3D imaging. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 1205-1212. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2010.5539829
Abstract: We present an imaging framework to acquire 3D surface scans at ultra high-resolutions (exceeding 600 samples per mm2). 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.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/41224
ISBN: 9781424469840
ISSN: 10636919
DOI: 10.1109/CVPR.2010.5539829
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