DocumentCode :
3402534
Title :
High-resolution modeling of moving and deforming objects using sparse geometric and dense photometric measurements
Author :
Xu, Yi ; Aliaga, Daniel G.
Author_Institution :
Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA
fYear :
2010
fDate :
13-18 June 2010
Firstpage :
1237
Lastpage :
1244
Abstract :
Modeling moving and deforming objects requires capturing as much information as possible during a very short time. When using off-the-shelf hardware, this often hinders the resolution and accuracy of the acquired model. Our key observation is that in as little as four frames both sparse surface-positional measurements and dense surface-orientation measurements can be acquired using a combination of structured light and photometric stereo, resulting in high-resolution models of moving and deforming objects. Our system projects alternating geometric and photometric patterns onto the object using a set of three projectors and captures the object using a synchronized camera. Small motion among temporally close frames is compensated by estimating the optical flow of images captured under the uniform illumination of the photometric light. Then spatial-temporal photogeometric reconstructions are performed to obtain dense and accurate point samples with a sampling resolution equal to that of the camera. Temporal coherence is also enforced. We demonstrate our system by successfully modeling several moving and deforming real-world objects.
Keywords :
computational geometry; image motion analysis; image reconstruction; image resolution; stereo image processing; dense photometric measurement; dense surface-orientation measurement; high-resolution modeling; optical flow; photometric stereo; sparse geometric measurement; sparse surface-positional measurement; spatial-temporal photogeometric reconstruction; structured light; temporal coherence; Cameras; Deformable models; Geometrical optics; Hardware; Image motion analysis; Lighting; Motion estimation; Optical variables control; Photometry; Solid modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on
Conference_Location :
San Francisco, CA
ISSN :
1063-6919
Print_ISBN :
978-1-4244-6984-0
Type :
conf
DOI :
10.1109/CVPR.2010.5539825
Filename :
5539825
Link To Document :
بازگشت