A Benchmark Dataset for 6DoF Object Pose Tracking

Po Chen Wu; Yueh Ying Lee; Hung Yu Tseng; Hsuan I. Ho; Ming Hsuan Yang; Shao Yi Chien

doi:10.1109/ISMAR-Adjunct.2017.62

A Benchmark Dataset for 6DoF Object Pose Tracking

Po Chen Wu, Yueh Ying Lee, Hung Yu Tseng, Hsuan I. Ho, Ming Hsuan Yang, Shao Yi Chien

Department of Computer Science

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

10 Citations (Scopus)

Abstract

Accurately tracking the six degree-of-freedom pose of an object in real scenes is an important task in computer vision and augmented reality with numerous applications. Although a variety of algorithms for this task have been proposed, it remains difficult to evaluate existing methods in the literature as oftentimes different sequences are used and no large benchmark datasets close to realworld scenarios are available. In this paper, we present a large object pose tracking benchmark dataset consisting of RGB-D video sequences of 2D and 3D targets with ground-truth information. The videos are recorded under various lighting conditions, different motion patterns and speeds with the help of a programmable robotic arm. We present extensive quantitative evaluation results of the state-of-the-art methods on this benchmark dataset and discuss the potential research directions in this field. The proposed benchmark dataset is available online at media.ee.ntu.edu.tw/research/OPT.

Original language	English
Title of host publication	Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
Editors	Wolfgang Broll, Holger Regenbrecht, J Edward Swan, Gerd Bruder, Myriam Servieres, Maki Sugimoto
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	186-191
Number of pages	6
ISBN (Electronic)	9780769563275
DOIs	https://doi.org/10.1109/ISMAR-Adjunct.2017.62
Publication status	Published - 2017 Oct 27
Event	16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017 - Nantes, France Duration: 2017 Oct 9 → 2017 Oct 13

Publication series

Name	Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017

Conference

Conference	16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
Country	France
City	Nantes
Period	17/10/9 → 17/10/13

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Human-Computer Interaction
Media Technology

Access to Document

10.1109/ISMAR-Adjunct.2017.62

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Wu, P. C., Lee, Y. Y., Tseng, H. Y., Ho, H. I., Yang, M. H., & Chien, S. Y. (2017). A Benchmark Dataset for 6DoF Object Pose Tracking. In W. Broll, H. Regenbrecht, J. E. Swan, G. Bruder, M. Servieres, & M. Sugimoto (Eds.), Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017 (pp. 186-191). [8088479] (Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISMAR-Adjunct.2017.62

Wu, Po Chen ; Lee, Yueh Ying ; Tseng, Hung Yu ; Ho, Hsuan I. ; Yang, Ming Hsuan ; Chien, Shao Yi. / A Benchmark Dataset for 6DoF Object Pose Tracking. Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017. editor / Wolfgang Broll ; Holger Regenbrecht ; J Edward Swan ; Gerd Bruder ; Myriam Servieres ; Maki Sugimoto. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 186-191 (Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017).

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abstract = "Accurately tracking the six degree-of-freedom pose of an object in real scenes is an important task in computer vision and augmented reality with numerous applications. Although a variety of algorithms for this task have been proposed, it remains difficult to evaluate existing methods in the literature as oftentimes different sequences are used and no large benchmark datasets close to realworld scenarios are available. In this paper, we present a large object pose tracking benchmark dataset consisting of RGB-D video sequences of 2D and 3D targets with ground-truth information. The videos are recorded under various lighting conditions, different motion patterns and speeds with the help of a programmable robotic arm. We present extensive quantitative evaluation results of the state-of-the-art methods on this benchmark dataset and discuss the potential research directions in this field. The proposed benchmark dataset is available online at media.ee.ntu.edu.tw/research/OPT.",

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Wu, PC, Lee, YY, Tseng, HY, Ho, HI, Yang, MH & Chien, SY 2017, A Benchmark Dataset for 6DoF Object Pose Tracking. in W Broll, H Regenbrecht, JE Swan, G Bruder, M Servieres & M Sugimoto (eds), Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017., 8088479, Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017, Institute of Electrical and Electronics Engineers Inc., pp. 186-191, 16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017, Nantes, France, 17/10/9. https://doi.org/10.1109/ISMAR-Adjunct.2017.62

A Benchmark Dataset for 6DoF Object Pose Tracking. / Wu, Po Chen; Lee, Yueh Ying; Tseng, Hung Yu; Ho, Hsuan I.; Yang, Ming Hsuan; Chien, Shao Yi.

Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017. ed. / Wolfgang Broll; Holger Regenbrecht; J Edward Swan; Gerd Bruder; Myriam Servieres; Maki Sugimoto. Institute of Electrical and Electronics Engineers Inc., 2017. p. 186-191 8088479 (Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017).

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

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Wu PC, Lee YY, Tseng HY, Ho HI, Yang MH, Chien SY. A Benchmark Dataset for 6DoF Object Pose Tracking. In Broll W, Regenbrecht H, Swan JE, Bruder G, Servieres M, Sugimoto M, editors, Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 186-191. 8088479. (Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017). https://doi.org/10.1109/ISMAR-Adjunct.2017.62

A Benchmark Dataset for 6DoF Object Pose Tracking

Abstract

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