A Multi-purpose RGB-D Dataset for Understanding Everyday Objects

Shuichi Akizuki, Manabu Hashimoto

Abstract

This paper introduces our ongoing work which is a project of establishing a novel dataset for the benchmarking of multiple robot vision tasks that aims to handle everyday objects. Our dataset is composed of 3D models, RGB-D input scenes and multi-type annotations. The 3D models are full-3D scan data of 100 everyday objects. Input scenes are over 54k RGB-D images that capture the table-top environment, including randomly placed everyday objects. Our dataset also provides four types of annotation: bounding boxes, affordance labels, object class labels, and 6 degrees of freedom (6DoF) poses. These are labeled for all objects in an image. These annotations are easily assigned to images via an original 6DoF annotation tool that has a simple graphical interface. We also report benchmarking results for modern object recognition algorithms.

Download


Paper Citation


in Harvard Style

Akizuki S. and Hashimoto M. (2020). A Multi-purpose RGB-D Dataset for Understanding Everyday Objects.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 470-475. DOI: 10.5220/0009142504700475


in Bibtex Style

@conference{visapp20,
author={Shuichi Akizuki and Manabu Hashimoto},
title={A Multi-purpose RGB-D Dataset for Understanding Everyday Objects},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={470-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009142504700475},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - A Multi-purpose RGB-D Dataset for Understanding Everyday Objects
SN - 978-989-758-402-2
AU - Akizuki S.
AU - Hashimoto M.
PY - 2020
SP - 470
EP - 475
DO - 10.5220/0009142504700475