@article{d75818a8f641498ab5bd0ebab3670782,
title = "Object Pose Estimation Incorporating Projection Loss and Discriminative Refinement",
abstract = "The accurate estimation of three-dimensional (3D) object pose is important in a wide range of applications, such as robotics and augmented reality. The key to estimate object poses is matching feature points in the captured image with predefined ones of the 3D model of the object. Existing learning-based pose estimation systems utilize a voting strategy to estimate the feature points in a vector space for improving the accuracy of the estimated pose. However, the loss function of such approaches only takes account of the direction of the vector, resulting in an error-prone localization of feature points. Therefore, this paper considers a projection loss function dealing with the error of the vector field and incorporates a refinement network to revise the predicted pose to obtain a good final output. Experimental results show that the proposed methods outperform the state-of-the-art methods in ADD(-S) metric on the LINEMOD and Occlusion LINEMOD datasets. Moreover, the proposed method can be applied to real-world practical scenarios in real time to simultaneously estimate the poses of multiple objects.",
keywords = "LINEMOD, Object pose estimation, convolutional neural network, deep learning, occlusion LINEMOD",
author = "You, {Jiun Kai} and Hsu, {Chen Chien James} and Wang, {Wei Yen} and Huang, {Shao Kang}",
note = "Funding Information: This work was supported in part by the Chinese Language and Technology Center of the National Taiwan Normal University (NTNU) through the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taiwan, and in part by the Ministry of Science and Technology, Taiwan, through the Pervasive Artificial Intelligence Research (PAIR) Labs, under Grant MOST 109-2634-F-003-006 and Grant MOST 109-2634-F-003-007. Funding Information: This work was supported in part by the Chinese Language and Technology Center of the National Taiwan Normal University (NTNU) through the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taiwan, and in part by the Ministry of Science and Technology, Taiwan, through the Pervasive Arti_cial Intelligence Research (PAIR) Labs, under Grant MOST 109-2634-F-003-006 and Grant MOST 109-2634-F-003-007. Publisher Copyright: {\textcopyright} 2013 IEEE.",
year = "2021",
doi = "10.1109/ACCESS.2021.3054493",
language = "English",
volume = "9",
pages = "18597--18606",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}