@inproceedings{6f61d17518624608b457aac030dc598a,
title = "A Benchmark Dataset for 6DoF Object Pose Tracking",
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.",
author = "Wu, {Po Chen} and Lee, {Yueh Ying} and Tseng, {Hung Yu} and Ho, {Hsuan I.} and Yang, {Ming Hsuan} and Chien, {Shao Yi}",
year = "2017",
month = oct,
day = "27",
doi = "10.1109/ISMAR-Adjunct.2017.62",
language = "English",
series = "Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "186--191",
editor = "Wolfgang Broll and Holger Regenbrecht and Swan, {J Edward} and Gerd Bruder and Myriam Servieres and Maki Sugimoto",
booktitle = "Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017",
address = "United States",
note = "16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017 ; Conference date: 09-10-2017 Through 13-10-2017",
}