kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On the Evaluation of RGB-D-Based Categorical Pose and Shape Estimation
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0001-8747-6359
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-1170-7162
2023 (English)In: Intelligent Autonomous Systems 17, IAS-17 / [ed] Petrovic, I Menegatti, E Markovic, I, Springer Nature , 2023, Vol. 577, p. 360-377Conference paper, Published paper (Refereed)
Abstract [en]

Recently, various methods for 6D pose and shape estimation of objects have been proposed. Typically, these methods evaluate their pose estimation in terms of average precision and reconstruction quality in terms of chamfer distance. In this work, we take a critical look at this predominant evaluation protocol, including metrics and datasets. We propose a new set of metrics, contribute new annotations for the Redwood dataset, and evaluate state-of-the-art methods in a fair comparison. We find that existing methods do not generalize well to unconstrained orientations and are actually heavily biased towards objects being upright. We provide an easy-to-use evaluation toolbox with well-defined metrics, method, and dataset interfaces, which allows evaluation and comparison with various state-of-the-art approaches (https://github.com/roym899/pose and shape evaluation).

Place, publisher, year, edition, pages
Springer Nature , 2023. Vol. 577, p. 360-377
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370
Keywords [en]
Pose estimation, Shape reconstruction, RGB-D-based perception
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-328417DOI: 10.1007/978-3-031-22216-0_25ISI: 000992458200025Scopus ID: 2-s2.0-85148744517OAI: oai:DiVA.org:kth-328417DiVA, id: diva2:1766817
Conference
17th International Conference on Intelligent Autonomous Systems (IAS), JUN 13-16, 2022, Zagreb, CROATIA
Note

QC 20230613

Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2023-06-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Bruns, LeonardJensfelt, Patric

Search in DiVA

By author/editor
Bruns, LeonardJensfelt, Patric
By organisation
Robotics, Perception and Learning, RPL
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 68 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf