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MirAIProjection: Real-time Projection onto High-speed Objects by Predicting Their 3D Position and Pose using DNNs

Published: 02 October 2020 Publication History

Abstract

Allowing projections on moving objects is associated with a problem that a projection might shift due to the delay between tracking and projection. In the present paper, we proposed a new prediction model based on deep neural networks that can be used to predict both pose and position of the target object. As a result, we developed a real-time tracking and projection system named"MirAIProjection that employs motion-capture cameras and common projectors. We conducted several experiments to evaluate the effectiveness of the proposed system and demonstrated that the proposed system could reduce the slipping and increase the accuracy and robustness of the projection.

References

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Cited By

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  • (2024)Real-Time Radiometric Compensation on Deforming Non-Rigid Surfaces using Motion Prediction in Projection Mappingプロジェクションマッピングにおける動き予測を用いた変形する非剛体への実時間光学的補正The Journal of The Institute of Image Information and Television Engineers10.3169/itej.78.34878:3(348-354)Online publication date: 2024
  • (2024)Projection mapping technologies: A review of current trends and future directionsProceedings of the Japan Academy, Series B10.2183/pjab.100.012100:3(234-251)Online publication date: 11-Mar-2024
  • (2022)3D Model-Based Tracking of Puppet in Depth Images for the Dynamic Video-Mapping of Its SuitOptoelectronic Devices in Robotic Systems10.1007/978-3-031-09791-1_1(1-21)Online publication date: 15-Jun-2022

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  1. MirAIProjection: Real-time Projection onto High-speed Objects by Predicting Their 3D Position and Pose using DNNs

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      cover image ACM Other conferences
      AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces
      September 2020
      613 pages
      ISBN:9781450375351
      DOI:10.1145/3399715
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 02 October 2020

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      Author Tags

      1. Deep neural network
      2. Dynamic projection
      3. Motion forecasting
      4. Projection mapping

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      • Short-paper
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      • Refereed limited

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      AVI '20
      AVI '20: International Conference on Advanced Visual Interfaces
      September 28 - October 2, 2020
      Salerno, Italy

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      AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;
      Overall Acceptance Rate 128 of 490 submissions, 26%

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      View all
      • (2024)Real-Time Radiometric Compensation on Deforming Non-Rigid Surfaces using Motion Prediction in Projection Mappingプロジェクションマッピングにおける動き予測を用いた変形する非剛体への実時間光学的補正The Journal of The Institute of Image Information and Television Engineers10.3169/itej.78.34878:3(348-354)Online publication date: 2024
      • (2024)Projection mapping technologies: A review of current trends and future directionsProceedings of the Japan Academy, Series B10.2183/pjab.100.012100:3(234-251)Online publication date: 11-Mar-2024
      • (2022)3D Model-Based Tracking of Puppet in Depth Images for the Dynamic Video-Mapping of Its SuitOptoelectronic Devices in Robotic Systems10.1007/978-3-031-09791-1_1(1-21)Online publication date: 15-Jun-2022

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