Localization Using Combinations of Model Views
.
Localization Using Combinations of Model Views.
In ICCV93, 226-230, 1993
Online Version
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Abstract
A method for localization, the act of recognizing the environment, is presented. The method is based on representing the scene as a set of 20 views and predicting the appearances of novel views by linear combinations of the model views. The method accurately approximates the appearance of scenes under weak perspective projection. Analysis of this projection as well as experimental results demonstrate that in many cases this approximation is sufficient to accurately describe the scene. When weak perspective approximation is invalid, either a larger number of models can be acquired or an iterative solution to account for the perspective distortions can be employed. The method has several advantages over other approaches. It uses relatively rich representations; the representations are 2D rather than 9D; and localization can be done from only a single 2D view.
Co-authors
Bibtex Entry
@inproceedings{BasriR93i-l,
title = {Localization Using Combinations of Model Views},
author = {Ronen Basri and Ehud Rivlin},
year = {1993},
booktitle = {ICCV93},
pages = {226-230},
abstract = {A method for localization, the act of recognizing the environment, is presented. The method is based on representing the scene as a set of 20 views and predicting the appearances of novel views by linear combinations of the model views. The method accurately approximates the appearance of scenes under weak perspective projection. Analysis of this projection as well as experimental results demonstrate that in many cases this approximation is sufficient to accurately describe the scene. When weak perspective approximation is invalid, either a larger number of models can be acquired or an iterative solution to account for the perspective distortions can be employed. The method has several advantages over other approaches. It uses relatively rich representations; the representations are 2D rather than 9D; and localization can be done from only a single 2D view.}
}