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 | |
| 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 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Human-Computer Interaction
- Media Technology
Cite this
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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
TY - GEN
T1 - A Benchmark Dataset for 6DoF Object Pose Tracking
AU - Wu, Po Chen
AU - Lee, Yueh Ying
AU - Tseng, Hung Yu
AU - Ho, Hsuan I.
AU - Yang, Ming Hsuan
AU - Chien, Shao Yi
PY - 2017/10/27
Y1 - 2017/10/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85040222900&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040222900&partnerID=8YFLogxK
U2 - 10.1109/ISMAR-Adjunct.2017.62
DO - 10.1109/ISMAR-Adjunct.2017.62
M3 - Conference contribution
AN - SCOPUS:85040222900
T3 - Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
SP - 186
EP - 191
BT - Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
A2 - Broll, Wolfgang
A2 - Regenbrecht, Holger
A2 - Swan, J Edward
A2 - Bruder, Gerd
A2 - Servieres, Myriam
A2 - Sugimoto, Maki
PB - Institute of Electrical and Electronics Engineers Inc.
ER -