Display methods of projection augmented reality based on deep learning pose estimation

Hyocheol Ro; Jung Hyun Byun; Yoon Jung Park; Tack Don Han

doi:10.1145/3306214.3338608

Display methods of projection augmented reality based on deep learning pose estimation

Hyocheol Ro, Jung Hyun Byun, Yoon Jung Park, Tack Don Han

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose three display methods for projection-based augmented reality. In spatial augmented reality (SAR), determining where information, objects, or contents are to be displayed is a difficult and important issue. We use deep learning models to estimate user pose and suggest ways to solve the issue based on this data. Finally, each method can be appropriately applied according to various the applications and scenarios.

Original language	English
Title of host publication	ACM SIGGRAPH 2019 Posters, SIGGRAPH 2019
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450363143
DOIs	https://doi.org/10.1145/3306214.3338608
Publication status	Published - 2019 Jul 28
Event	ACM SIGGRAPH 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2019 - Los Angeles, United States Duration: 2019 Jul 28 → …

Publication series

Name	ACM SIGGRAPH 2019 Posters, SIGGRAPH 2019

Conference

Conference	ACM SIGGRAPH 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2019
Country	United States
City	Los Angeles
Period	19/7/28 → …

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.NRF-2018R1A2A1A05078628).

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).

All Science Journal Classification (ASJC) codes

Computer Graphics and Computer-Aided Design
Human-Computer Interaction

Access to Document

10.1145/3306214.3338608

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title = "Display methods of projection augmented reality based on deep learning pose estimation",

abstract = "In this paper, we propose three display methods for projection-based augmented reality. In spatial augmented reality (SAR), determining where information, objects, or contents are to be displayed is a difficult and important issue. We use deep learning models to estimate user pose and suggest ways to solve the issue based on this data. Finally, each method can be appropriately applied according to various the applications and scenarios.",

author = "Hyocheol Ro and Byun, {Jung Hyun} and Park, {Yoon Jung} and Han, {Tack Don}",

note = "Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.NRF-2018R1A2A1A05078628). Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; ACM SIGGRAPH 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2019 ; Conference date: 28-07-2019",

year = "2019",

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doi = "10.1145/3306214.3338608",

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Ro, H, Byun, JH, Park, YJ & Han, TD 2019, Display methods of projection augmented reality based on deep learning pose estimation. in ACM SIGGRAPH 2019 Posters, SIGGRAPH 2019. ACM SIGGRAPH 2019 Posters, SIGGRAPH 2019, Association for Computing Machinery, Inc, ACM SIGGRAPH 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2019, Los Angeles, United States, 19/7/28. https://doi.org/10.1145/3306214.3338608

Display methods of projection augmented reality based on deep learning pose estimation. / Ro, Hyocheol; Byun, Jung Hyun; Park, Yoon Jung; Han, Tack Don.

ACM SIGGRAPH 2019 Posters, SIGGRAPH 2019. Association for Computing Machinery, Inc, 2019. (ACM SIGGRAPH 2019 Posters, SIGGRAPH 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.NRF-2018R1A2A1A05078628). Publisher Copyright: © 2019 Copyright held by the owner/author(s).

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AB - In this paper, we propose three display methods for projection-based augmented reality. In spatial augmented reality (SAR), determining where information, objects, or contents are to be displayed is a difficult and important issue. We use deep learning models to estimate user pose and suggest ways to solve the issue based on this data. Finally, each method can be appropriately applied according to various the applications and scenarios.

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Display methods of projection augmented reality based on deep learning pose estimation

Abstract

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Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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