Hybrid 6D Object Pose Estimation from the RGB Image

Rafal Staszak, Dominik Belter

Abstract

In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approach. We use separate deep neural networks to: detect the object on the image, estimate the center of the object, and estimate the translation and ”in-place” rotation of the object. Then, we use geometrical relations on the image and the camera model to recover the full 6D object pose. As a result, we avoid the direct estimation of the object orientation defined in SO3 using a neural network. We propose the 4D-NET neural network to estimate translation and ”in-place” rotation of the object. Finally, we show results on the images generated from the Pascal VOC and ShapeNet datasets.

Download


Paper Citation


in Harvard Style

Staszak R. and Belter D. (2019). Hybrid 6D Object Pose Estimation from the RGB Image.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 541-549. DOI: 10.5220/0007933105410549


in Bibtex Style

@conference{icinco19,
author={Rafal Staszak and Dominik Belter},
title={Hybrid 6D Object Pose Estimation from the RGB Image},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={541-549},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007933105410549},
isbn={978-989-758-380-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Hybrid 6D Object Pose Estimation from the RGB Image
SN - 978-989-758-380-3
AU - Staszak R.
AU - Belter D.
PY - 2019
SP - 541
EP - 549
DO - 10.5220/0007933105410549