High-Resolution Modeling of Moving and Deforming Objects Using Sparse Geometric and Dense Photometric Measurements

被引:5
|
作者
Xu, Yi [1 ]
Aliaga, Daniel G. [1 ]
机构
[1] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA
来源
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2010年
关键词
STEREO;
D O I
10.1109/CVPR.2010.5539825
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1237 / 1244
页数:8
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