6D0F pose estimation for similar objects using spatial relationship of part-affordance

Shuichi Akizuki, Yoshimitsu Aoki

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a method to estimate 6D pose (3D position and pose) of the everyday objects, especially Kitchen or DIY tools, even if there are not the same 3D model of the target object. We assume that the tools that belong to same category have common spatial relation in part-affordances, the role of each part. The proposed method uses this assumption for pose estimation. By minimizing two types of consistencies, the spatial relationship of part-affordances and the shapes, we have confirmed that the proposed method achieved higher performance compared with traditional pose estimation method.

Original languageEnglish
Pages (from-to)107-112
Number of pages6
JournalSeimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
Volume85
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

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Kitchens

Keywords

  • 3D object recognition
  • Affordance
  • Point cloud processing
  • Pose estimation

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

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abstract = "In this paper, we propose a method to estimate 6D pose (3D position and pose) of the everyday objects, especially Kitchen or DIY tools, even if there are not the same 3D model of the target object. We assume that the tools that belong to same category have common spatial relation in part-affordances, the role of each part. The proposed method uses this assumption for pose estimation. By minimizing two types of consistencies, the spatial relationship of part-affordances and the shapes, we have confirmed that the proposed method achieved higher performance compared with traditional pose estimation method.",
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AB - In this paper, we propose a method to estimate 6D pose (3D position and pose) of the everyday objects, especially Kitchen or DIY tools, even if there are not the same 3D model of the target object. We assume that the tools that belong to same category have common spatial relation in part-affordances, the role of each part. The proposed method uses this assumption for pose estimation. By minimizing two types of consistencies, the spatial relationship of part-affordances and the shapes, we have confirmed that the proposed method achieved higher performance compared with traditional pose estimation method.

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