6-DoF Pose Estimation and CAD Model Retrieval for XR Interface from a Single RGB Image

Sieun Park, Wonje Jeong, Soon Yong Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a 6-DoF pose estimation and CAD model retrieval for XR interface from a single RGB image. A deep learning network is used to estimate the 6-DoF pose of a real object in an RGB image. Then, several CAD model candidates are rendered and their similarity with the image is measured to decide the final matching CAD model. In the last, the rendered image of the matching CAD model is overlayed with the RGB image. In the experiment, total ten object categories are tested and evaluated using two deep networks.

Original languageEnglish
Title of host publicationProceedings of the 2024 International Conference on Advanced Visual Interfaces, AVI 2024
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400717642
DOIs
StatePublished - 3 Jun 2024
Event2024 International Conference on Advanced Visual Interfaces, AVI 2024 - Arenzano, Genoa, Italy
Duration: 3 Jun 20247 Jun 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2024 International Conference on Advanced Visual Interfaces, AVI 2024
Country/TerritoryItaly
CityArenzano, Genoa
Period3/06/247/06/24

Keywords

  • AR/XR
  • deep learning
  • pose estimation
  • similarity measure

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