A General Framework for Fast 3D Object Detection and Localization Using an Uncalibrated Camera
Abstract
In this paper, we present a real-time approach for 3D object detection using a single, mobile and uncalibrated camera. We develop our algorithm using a feature-based method based on two novel naive Bayes classifiers for viewpoint and feature matching. Our algorithm exploits the specific structure of various binary descriptors in order to boost feature matching by conserving descriptor properties (e.g., rotational and scale invariance, robustness to illumination variations and real-time performance). Unlike state-of-the-art methods, our novel naive classifiers only require a database with a small memory footprint because we store efficiently encoded features. In addition, we also improve the indexing scheme to speed up the matching process. Because our database is built from powerful descriptors, only a few images need to be 'learned' and constructing a database for a new object is highly efficient.
Cite
Text
Montero et al. "A General Framework for Fast 3D Object Detection and Localization Using an Uncalibrated Camera." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.122Markdown
[Montero et al. "A General Framework for Fast 3D Object Detection and Localization Using an Uncalibrated Camera." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/montero2015wacv-general/) doi:10.1109/WACV.2015.122BibTeX
@inproceedings{montero2015wacv-general,
title = {{A General Framework for Fast 3D Object Detection and Localization Using an Uncalibrated Camera}},
author = {Montero, Andres Solis and Lang, Jochen and Laganière, Robert},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2015},
pages = {884-891},
doi = {10.1109/WACV.2015.122},
url = {https://mlanthology.org/wacv/2015/montero2015wacv-general/}
}