abstract

Learning-based Estimation of 6-DoF Camera Poses from Partial Observation of Large Objects for Mobile AR*

Authors:

Jean-Pierre Lomaliza,

Hanhoon ParkAuthors Info & Claims

VRST '19: Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology

Article No.: 78, Pages 1 - 2

https://doi.org/10.1145/3359996.3364718

Published: 12 November 2019 Publication History

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Abstract

We propose a method that estimates 6-DoF camera pose from a partially visible large object, by exploiting information of its subparts that are detected using a state-of-the-art convolutional neural network (CNN). The trained CNN outputs two-dimensional bounding boxes around subparts and associated classes. Information from detection is then fed to a deep neural network that regresses to camera's 6-DoF poses. Experimental results show that the proposed method is more robust to occlusions than conventional learning-based methods.

References

[1]

V. A. Prisacariu and I. D. Reid (2012). PWP3D: Real-time Segmentation and Tracking of 3D Objects. IJCV, 98(3), 335–354.

Digital Library

Google Scholar

[2]

H. Tjaden, U. Schwanecke, and E. Schomer (2016). Real-time Monocular Segmentation and Pose Tracking of Multiple Objects. Proceedings of ECCV.

Crossref

Google Scholar

[3]

Henning T, Ulrich S., Elmar S. (2017). Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms. Proceedings of ICCV, 124-132.

Google Scholar

[4]

Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox (2018). PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes. Proceedings of RSS.

Crossref

Google Scholar

[5]

B. Tekin, S. N. Sinha, and P. Fua (2018). Real-Time Seamless Single Shot 6D Object Pose Prediction. Proceedings of CVPR.

Crossref

Google Scholar

[6]

O. Akgul, H. I. Penekli, and Y. Genc (2016). Applying Deep Learning in Augmented Reality Tracking. Proceedings of SITIS.

Crossref

Google Scholar

[7]

B. Seo, H. Park, J. Park, S. Hinterstoisser, and S. Ilic (2014). Optimal Local Searching for Fast and Robust Textureless 3D Object Tracking in Highly Cluttered Backgrounds. TVCG, 20(1), 99– 110.

Digital Library

Google Scholar

[8]

Abadi M., Barham P., Chen J., Chen Z., Davis A., Dean J., & Kudlur M. (2016). Tensorflow: A system for large-scale machine learning. In 12th Symposium on Operating Systems Design and Implementation.

Google Scholar

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Uddin MMukherjee SKodialam MLakshman T(2020)GLAMAR: Geo-Location Assisted Mobile Augmented Reality for Industrial Automation2020 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC50012.2020.00036(232-245)Online publication date: Nov-2020
https://doi.org/10.1109/SEC50012.2020.00036

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Information

Published In

VRST '19: Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology

November 2019

498 pages

ISBN:9781450370011

DOI:10.1145/3359996

Editors:
Tomas Trescak
Western Sydney University
,
Simeon Simoff
Western Sydney University
,
Deborah Richards
Macquarie University
,
Anton Bogdanovych
Western Sydney University
,
Thierry Duval
IMT Atlantique
,
Torsten Kuhlen
RWTH Aachen University
,
Huyen Nguyen
University of New South Wales
,
Shigeo Morishima
University of Waseda
,
Yuichi Itoh
University of Osaka
,
Richard Skarbez
La Trobe
,
Anton Bogdanovych
Western Sydney University
,
Martin Masek
Edith Cowan University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 12 November 2019

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VRST '19: 25th ACM Symposium on Virtual Reality Software and Technology

November 12 - 15, 2019

NSW, Parramatta, Australia

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Uddin MMukherjee SKodialam MLakshman T(2020)GLAMAR: Geo-Location Assisted Mobile Augmented Reality for Industrial Automation2020 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC50012.2020.00036(232-245)Online publication date: Nov-2020
https://doi.org/10.1109/SEC50012.2020.00036

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Initial Pose Estimation of 3D Object with Severe Occlusion Using Deep Learning

Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system

Binocular mobile augmented reality based on stereo camera tracking

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