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18th International Conference on Pattern Recognition (ICPR'06) Volume 1
An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
ASCII Text
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Stefan Wenhardt, Benjamin Deutsch, Joachim Hornegger, Heinrich Niemann, Joachim Denzler, "An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction," Pattern Recognition, International Conference on, vol. 1, pp. 103-106, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006.
 
 
BibTex
x
 
@article{ 10.1109/ICPR.2006.253,
author = {Stefan Wenhardt and Benjamin Deutsch and Joachim Hornegger and Heinrich Niemann and Joachim Denzler},
title = {An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2006},
issn = {1051-4651},
pages = {103-106},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.253},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction
SN - 1051-4651
SP103
EP106
A1 - Stefan Wenhardt,
A1 - Benjamin Deutsch,
A1 - Joachim Hornegger,
A1 - Heinrich Niemann,
A1 - Joachim Denzler,
PY - 2006
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Stefan Wenhardt, Friedrich-Alexander University of Erlangen, Germany
Benjamin Deutsch, Friedrich-Alexander University of Erlangen, Germany
Joachim Hornegger, Friedrich-Alexander University of Erlangen, Germany
Heinrich Niemann, Friedrich-Alexander University of Erlangen, Germany
Joachim Denzler, Friedrich Schiller University of Jena

We present an algorithm for optimal view point selection for 3-D reconstruction of an object using 2-D image points. Since the image points are noisy, a Kalman filter is used to obtain the best estimate of the object?s geometry. This Kalman filter allows us to efficiently predict the effect of any given camera position on the uncertainty, and therefore quality, of the estimate. By choosing a suitable optimization criterion, we are able to determine the camera positions which minimize our reconstruction error.

We verify our results using two experiments with real images: one experiment uses a calibration pattern for comparison to a ground-truth state, the other reconstructs a real world object.

Citation:
Stefan Wenhardt, Benjamin Deutsch, Joachim Hornegger, Heinrich Niemann, Joachim Denzler, "An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction," icpr, vol. 1, pp.103-106, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006
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