Publication detail

Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans

GAJDOŠECH, L. KOCUR, V. STUCHLÍK, M. HUDEC, L. MADARAS, M.

Original Title

Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans

Type

conference paper

Language

English

Original Abstract

An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a  baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.

Keywords

Computer Vision, Bin Pose Estimation, 6D Pose Estimation, Deep Learning, Point Clouds

Authors

GAJDOŠECH, L.; KOCUR, V.; STUCHLÍK, M.; HUDEC, L.; MADARAS, M.

Released

6. 2. 2022

Publisher

SciTePress - Science and Technology Publications

Location

Setubal

ISBN

978-989-758-555-5

Book

Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP

Pages from

545

Pages to

552

Pages count

8

URL

https://www.scitepress.org/Link.aspx?doi=10.5220/0010878200003124

BibTex

@inproceedings{BUT182954,
  author="Lukáš {Gajdošech} and Viktor {Kocur} and Martin {Stuchlík} and Lukáš {Hudec} and Martin {Madaras}",
  title="Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans",
  booktitle="Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP",
  year="2022",
  pages="545--552",
  publisher="SciTePress - Science and Technology Publications",
  address="Setubal",
  doi="10.5220/0010878200003124",
  isbn="978-989-758-555-5",
  url="https://www.scitepress.org/Link.aspx?doi=10.5220/0010878200003124"
}