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Authors: Frederik Haarslev ; William Juel ; Norbert Krüger and Leon Bodenhagen

Affiliation: SDU Robotics, Maersk Mc-Kinney Molle Institute, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark

Keyword(s): Synthetic Ground Truth Generation, Object Segmentation, Pose Estimation.

Abstract: We present a method for generating synthetic ground truth for training segmentation networks for presegmenting point clouds in pose estimation problems. Our method replaces global pose estimation algorithms such as RANSAC which requires manual fine-tuning with a robust CNN, without having to hand-label segmentation masks for the given object. The data is generated by blending cropped images of the objects with arbitrary backgrounds. We test the method in two scenarios, and show that networks trained on the generated data segments the objects with high accuracy, allowing them to be used in a pose estimation pipeline.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Haarslev, F.; Juel, W.; Krüger, N. and Bodenhagen, L. (2020). Synthetic Ground Truth for Presegmentation of Known Objects for Effortless Pose Estimation. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 482-489. DOI: 10.5220/0009163904820489

@conference{visapp20,
author={Frederik Haarslev. and William Juel. and Norbert Krüger. and Leon Bodenhagen.},
title={Synthetic Ground Truth for Presegmentation of Known Objects for Effortless Pose Estimation},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={482-489},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009163904820489},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Synthetic Ground Truth for Presegmentation of Known Objects for Effortless Pose Estimation
SN - 978-989-758-402-2
IS - 2184-4321
AU - Haarslev, F.
AU - Juel, W.
AU - Krüger, N.
AU - Bodenhagen, L.
PY - 2020
SP - 482
EP - 489
DO - 10.5220/0009163904820489