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Abstract: We propose a novel model-based algorithm which finds the 3D pose of an object from an image by breaking down the estimation process into two linear processing stages, namely the depth recovery and the pose calculation. The depth recovery stage determines the new positions of the model point set in 3D space whereas the pose calculation step is a least-square estimation of the transformation parameters between the point set formed from the previous stage and the model set. (Update)
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BibTeX entry: (Update)
Or S.H, Luk W.S., et al., "An efficient iterative pose estimation algorithm", Image and Vision Computing Journal, 16(5) , pp.355-364, 1998 http://citeseer.ist.psu.edu/article/or98efficient.html More
@inproceedings{ or98efficient,
author = "Siu-Hang Or and W. S. Luk and K. H. Wong and Irwin King",
title = "An Efficient Iterative Pose Estimation Algorithm",
booktitle = "{ACCV} (2)",
pages = "559-566",
year = "1998",
url = "citeseer.ist.psu.edu/article/or98efficient.html" }
Citations (may not include all citations):267 Closed-form solution of absolute orientation using unit quat.. (context) - Horn - 1987
98 Model-based object pose in 25 lines of code - Dementhon, Davis - 1995
81 Optimal motion and structure estimation (context) - Weng, Ahuja et al. - 1993
78 Least-square fitting of two3-dpoint sets (context) - Arun, Huang et al. - 1987
56 motion estimation in model-based facial image coding (context) - Li, Roivainen et al. - 1993
55 Recursive estimation of motion (context) - Azarbayejani, Pentland - 1995
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