@inproceedings{803569e428044073ba589dd5f11c8148,
title = "Dynamic Graph CNN with Attention Module for 3D Hand Pose Estimation",
abstract = "Recently, 3D hand pose estimation methods taking point cloud as input show the most advanced performance. We present a new 3D deep learning hand pose estimation network for an unordered point cloud. Our approach utilizes EdgeConv layer as the basic element, where an attention embedding version EdgeConv layer is proposed for feature extraction in hand pose estimation task. To improve the result, we design a hand pose improvement network that inputs points whose are in the neighbor of the estimated fingers and outputs a rectify hand pose. We evaluate our method on several famous datasets to prove that our method can get excellent result compared to some most advanced methods.",
keywords = "3D hand pose estimation, Attention embedding module, Point cloud",
author = "Xu Jiang and Xiaohong Ma",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 16th International Symposium on Neural Networks, ISNN 2019 ; Conference date: 10-07-2019 Through 12-07-2019",
year = "2019",
doi = "10.1007/978-3-030-22796-8\_10",
language = "英语",
isbn = "9783030227951",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "87--96",
editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",
booktitle = "Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings",
address = "德国",
}