Feature-augmented Trained Models for 6DOF Object Recognition and Camera Calibration

Kripasindhu Sarkar, Alain Pagani, Didier Stricker

2016

Abstract

In this paper we address the problem in the offline stage of 3D modelling in feature based object recognition. While the online stage of recognition - feature matching and pose estimation, has been refined several times over the past decade incorporating filters and heuristics for robust and scalable recognition, the offline stage of creating feature based models remained unchanged. In this work we take advantage of the easily available 3D scanners and 3D model databases like 3D-warehouse, and use them as our source of input for 3D CAD models of real objects. We process on the CAD models to produce feature-augmented trained models which can be used by any online recognition stage of object recognition. These trained models can also be directly used as a calibration rig for performing camera calibration from a single image. The evaluation shows that our fully automatically created feature-augmented trained models perform better in terms of recognition recall over the baseline - which is the tedious manual way of creating feature models. When used as a calibration rig, our feature augmented models achieve comparable accuracy with the popular camera-calibration techniques thereby making them an easy and quick way of performing camera calibration.

Paper Citation

in Harvard Style

Sarkar K., Pagani A. and Stricker D. (2016). Feature-augmented Trained Models for 6DOF Object Recognition and Camera Calibration . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 632-640. DOI: 10.5220/0005781106320640

in Bibtex Style

@conference{visapp16,
author={Kripasindhu Sarkar and Alain Pagani and Didier Stricker},
title={Feature-augmented Trained Models for 6DOF Object Recognition and Camera Calibration},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={632-640},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005781106320640},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Feature-augmented Trained Models for 6DOF Object Recognition and Camera Calibration
SN - 978-989-758-175-5
AU - Sarkar K.
AU - Pagani A.
AU - Stricker D.
PY - 2016
SP - 632
EP - 640
DO - 10.5220/0005781106320640