SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences

Dennis Stumpf, Stephan Krauß, Gerd Reis, Oliver Wasenmüller, Didier Stricker, Didier Stricker

2021

Abstract

Large labeled data sets are one of the essential basics of modern deep learning techniques. Therefore, there is an increasing need for tools that allow to label large amounts of data as intuitively as possible. In this paper, we introduce SALT, a tool to semi-automatically annotate RGB-D video sequences to generate 3D bounding boxes for full six Degrees of Freedom (DoF) object poses, as well as pixel-level instance segmentation masks for both RGB and depth. Besides bounding box propagation through various interpolation techniques, as well as algorithmically guided instance segmentation, our pipeline also provides built-in pre-processing functionalities to facilitate the data set creation process. By making full use of SALT, annotation time can be reduced by a factor of up to 33.95 for bounding box creation and 8.55 for RGB segmentation without compromising the quality of the automatically generated ground truth.

Paper Citation

in Harvard Style

Stumpf D., Krauß S., Reis G., Wasenmüller O. and Stricker D. (2021). SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 595-603. DOI: 10.5220/0010303005950603

in Bibtex Style

@conference{visapp21,
author={Dennis Stumpf and Stephan Krauß and Gerd Reis and Oliver Wasenmüller and Didier Stricker},
title={SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={595-603},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010303005950603},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences
SN - 978-989-758-488-6
AU - Stumpf D.
AU - Krauß S.
AU - Reis G.
AU - Wasenmüller O.
AU - Stricker D.
PY - 2021
SP - 595
EP - 603
DO - 10.5220/0010303005950603
PB - SciTePress