Hybrid Weak-Perspective and Full-Perspective Matching
Abstract
Full-perspective mappings between 3-D objects and 2-D images are more complicated than weak-perspective mappings, which consider only rotation, translation, and scaling. Therefore, in 3-D model-based robot navigation, it is important to understand how and when full-perspective must be taken into account. A probabilistic combinatorial optimization algorithm is used to search for an optimal match between 3-D landmarks and 2-D image features. Three variations are considered. A weak-perspective algorithm rotates, translates, and scales an initial 2-D projection of the 3-D landmark. A full perspective selects a most promising alternative, but then updates the pose and reprojects the landmark. Like the full-perspective algorithm, the hybrid algorithm reliably recovers the true pose of the robot, and like the weak-perspective algorithm, it runs 5 to 10 faster than the full-perspective algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Beveridge and Riseman. "Hybrid Weak-Perspective and Full-Perspective Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992. doi:10.1109/CVPR.1992.223154Markdown
[Beveridge and Riseman. "Hybrid Weak-Perspective and Full-Perspective Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992.](https://mlanthology.org/cvpr/1992/beveridge1992cvpr-hybrid/) doi:10.1109/CVPR.1992.223154BibTeX
@inproceedings{beveridge1992cvpr-hybrid,
title = {{Hybrid Weak-Perspective and Full-Perspective Matching}},
author = {Beveridge, J. Ross and Riseman, Edward M.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1992},
pages = {432-438},
doi = {10.1109/CVPR.1992.223154},
url = {https://mlanthology.org/cvpr/1992/beveridge1992cvpr-hybrid/}
}