Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

A shared pose regression network for pose estimation of objects from RGB images

Hein Bengtson, Stefan LU ; Åström, Hampus LU orcid ; Moeslund, Thomas B. ; Topp, Elin A. LU orcid and Krueger, Volker LU orcid (2022) IEEE/RSJ International Conference on Signal Image Technology & Internet Based Systems (SITIS) 2022
Abstract
In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose... (More)
In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Training, Measurement, Solid modeling, image resolution, Pose estimation, neural networks, memory management
host publication
IEEE/RSJ International Conference on Signal Image Technology & Internet Based Systems (SITIS)
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE/RSJ International Conference on Signal Image Technology & Internet Based Systems (SITIS) 2022
conference location
Dijon, France
conference dates
2022-10-19 - 2022-10-21
external identifiers
  • scopus:85154031291
ISBN
978-1-6654-6496-3
978-1-6654-6495-6
DOI
10.1109/SITIS57111.2022.00022
project
RobotLab LTH
WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
language
English
LU publication?
yes
id
1c75c100-5dc0-465a-85c3-d076f7b584a1
date added to LUP
2023-07-20 16:38:08
date last changed
2025-10-14 10:21:52
@inproceedings{1c75c100-5dc0-465a-85c3-d076f7b584a1,
  abstract     = {{In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance.}},
  author       = {{Hein Bengtson, Stefan and Åström, Hampus and Moeslund, Thomas B. and Topp, Elin A. and Krueger, Volker}},
  booktitle    = {{IEEE/RSJ International Conference on Signal Image Technology & Internet Based Systems (SITIS)}},
  isbn         = {{978-1-6654-6496-3}},
  keywords     = {{Training; Measurement; Solid modeling; image resolution; Pose estimation; neural networks; memory management}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{A shared pose regression network for pose estimation of objects from RGB images}},
  url          = {{http://dx.doi.org/10.1109/SITIS57111.2022.00022}},
  doi          = {{10.1109/SITIS57111.2022.00022}},
  year         = {{2022}},
}