Semi-automatic Training Data Generation for Semantic Segmentation using 6DoF Pose Estimation

Shuichi Akizuki, Manabu Hashimoto

2019

Abstract

In this research, we propose a method using a low cost process to generate large volumes of real images as training data for semantic segmentation. The method first estimates the six-degree-of-freedom (6DoF) pose for objects in images obtained using an RGB-D sensor, and then maps labels that have been pre-assigned to 3D models onto the images. It also captures additional input images while the camera is moving, and is able to map labels to these other input images based on the relative motion of the viewpoint. This method has made it possible to obtain large volumes of ground truth data for real images. The proposed method has been used to create a new publicity available dataset for affordance segmentation, called the NEDO Part-Affordance Dataset v1, which has been used to benchmark some typical semantic segmentation algorithms.

Paper Citation

in Harvard Style

Akizuki S. and Hashimoto M. (2019). Semi-automatic Training Data Generation for Semantic Segmentation using 6DoF Pose Estimation. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 607-613. DOI: 10.5220/0007568706070613

in Bibtex Style

@conference{visapp19,
author={Shuichi Akizuki and Manabu Hashimoto},
title={Semi-automatic Training Data Generation for Semantic Segmentation using 6DoF Pose Estimation},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={607-613},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007568706070613},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Semi-automatic Training Data Generation for Semantic Segmentation using 6DoF Pose Estimation
SN - 978-989-758-354-4
AU - Akizuki S.
AU - Hashimoto M.
PY - 2019
SP - 607
EP - 613
DO - 10.5220/0007568706070613
PB - SciTePress