loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Kripasindhu Sarkar 1 ; Kiran Varanasi 2 and Didier Stricker 1

Affiliations: 1 German Research Center for Artificial Intelligence (DFKI) and Technical University Kaiserslautern, Germany ; 2 German Research Center for Artificial Intelligence (DFKI), Germany

Keyword(s): Object Recognition, Fine-tuning CNNs, Domain Fusion, Training on 3D Data, Graphics Assisted CNN.

Abstract: We present a method for 3D object recognition in 2D images which uses 3D models as the only source of the training data. Our method is particularly useful when a 3D CAD object or a scan needs to be identified in a catalogue form a given query image; where we significantly cut down the overhead of manual labeling. We take virtual snapshots of the available 3D models by a computer graphics pipeline and fine-tune existing pretrained CNN models for our object categories. Experiments show that our method performs better than the existing local-feature based recognition system in terms of recognition recall.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.222.146.114

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Sarkar, K.; Varanasi, K. and Stricker, D. (2017). Trained 3D Models for CNN based Object Recognition. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP; ISBN 978-989-758-226-4; ISSN 2184-4321, SciTePress, pages 130-137. DOI: 10.5220/0006272901300137

@conference{visapp17,
author={Kripasindhu Sarkar. and Kiran Varanasi. and Didier Stricker.},
title={Trained 3D Models for CNN based Object Recognition},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={130-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006272901300137},
isbn={978-989-758-226-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP
TI - Trained 3D Models for CNN based Object Recognition
SN - 978-989-758-226-4
IS - 2184-4321
AU - Sarkar, K.
AU - Varanasi, K.
AU - Stricker, D.
PY - 2017
SP - 130
EP - 137
DO - 10.5220/0006272901300137
PB - SciTePress