PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud

Jia Hong Chen; Chen Chien Hsu

doi:10.1109/ICSSE52999.2021.9538459

PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud

Jia Hong Chen, Chen Chien Hsu^*

^*Corresponding author for this work

Department of Electrical Engineering

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

Abstract

This paper provides a novel method called "PointCNN-Hand"for 3D hand joints estimation based on PointCNN. To use the depth image effectively, we transfer the hand depth image into the 3D hand cloud point and implement end-to-end training by PointCNN-Hand for hand joint estimation. We then perform error analysis on MSRA, NYU, and ICVL datasets to compare with the state-of-the-art methods. The experiments show that the proposed method has desired results, and the model parameters are relatively smaller than those of other methods. To be specific, the parameters of the proposed PointCNN-Hand network are reduced to only 3 Mega Byte (MB) with Floating Point Operations (FLOPs) less than 232.05M.

Original language	English
Title of host publication	Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	458-463
Number of pages	6
ISBN (Electronic)	9781665448482
DOIs	https://doi.org/10.1109/ICSSE52999.2021.9538459
Publication status	Published - 2021 Aug 26
Event	2021 International Conference on System Science and Engineering, ICSSE 2021 - Virtual, Ho Chi Minh City, Viet Nam Duration: 2021 Aug 26 → 2021 Aug 28

Publication series

Name	Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021

Conference

Conference	2021 International Conference on System Science and Engineering, ICSSE 2021
Country/Territory	Viet Nam
City	Virtual, Ho Chi Minh City
Period	2021/08/26 → 2021/08/28

Keywords

3D hand pose estimation
Convolutional Neural Network
Hand articulation

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Signal Processing
Energy Engineering and Power Technology
Control and Systems Engineering
Electrical and Electronic Engineering
Control and Optimization

Access to Document

10.1109/ICSSE52999.2021.9538459

Cite this

Chen, J. H., & Hsu, C. C. (2021). PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud. In Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021 (pp. 458-463). (Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSSE52999.2021.9538459

PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud. / Chen, Jia Hong; Hsu, Chen Chien.
Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 458-463 (Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021).

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

Chen, JH & Hsu, CC 2021, PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud. in Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021. Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 458-463, 2021 International Conference on System Science and Engineering, ICSSE 2021, Virtual, Ho Chi Minh City, Viet Nam, 2021/08/26. https://doi.org/10.1109/ICSSE52999.2021.9538459

Chen JH, Hsu CC. PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud. In Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 458-463. (Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021). doi: 10.1109/ICSSE52999.2021.9538459

Chen, Jia Hong ; Hsu, Chen Chien. / PointCNN-Hand : 3D hand joints estimate by PointCNN from hand point cloud. Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 458-463 (Proceedings of 2021 International Conference on System Science and Engineering, ICSSE 2021).

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title = "PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud",

abstract = "This paper provides a novel method called {"}PointCNN-Hand{"}for 3D hand joints estimation based on PointCNN. To use the depth image effectively, we transfer the hand depth image into the 3D hand cloud point and implement end-to-end training by PointCNN-Hand for hand joint estimation. We then perform error analysis on MSRA, NYU, and ICVL datasets to compare with the state-of-the-art methods. The experiments show that the proposed method has desired results, and the model parameters are relatively smaller than those of other methods. To be specific, the parameters of the proposed PointCNN-Hand network are reduced to only 3 Mega Byte (MB) with Floating Point Operations (FLOPs) less than 232.05M.",

keywords = "3D hand pose estimation, Convolutional Neural Network, Hand articulation",

author = "Chen, {Jia Hong} and Hsu, {Chen Chien}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on System Science and Engineering, ICSSE 2021 ; Conference date: 26-08-2021 Through 28-08-2021",

year = "2021",

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doi = "10.1109/ICSSE52999.2021.9538459",

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T2 - 2021 International Conference on System Science and Engineering, ICSSE 2021

AU - Chen, Jia Hong

AU - Hsu, Chen Chien

PY - 2021/8/26

Y1 - 2021/8/26

N2 - This paper provides a novel method called "PointCNN-Hand"for 3D hand joints estimation based on PointCNN. To use the depth image effectively, we transfer the hand depth image into the 3D hand cloud point and implement end-to-end training by PointCNN-Hand for hand joint estimation. We then perform error analysis on MSRA, NYU, and ICVL datasets to compare with the state-of-the-art methods. The experiments show that the proposed method has desired results, and the model parameters are relatively smaller than those of other methods. To be specific, the parameters of the proposed PointCNN-Hand network are reduced to only 3 Mega Byte (MB) with Floating Point Operations (FLOPs) less than 232.05M.

AB - This paper provides a novel method called "PointCNN-Hand"for 3D hand joints estimation based on PointCNN. To use the depth image effectively, we transfer the hand depth image into the 3D hand cloud point and implement end-to-end training by PointCNN-Hand for hand joint estimation. We then perform error analysis on MSRA, NYU, and ICVL datasets to compare with the state-of-the-art methods. The experiments show that the proposed method has desired results, and the model parameters are relatively smaller than those of other methods. To be specific, the parameters of the proposed PointCNN-Hand network are reduced to only 3 Mega Byte (MB) with Floating Point Operations (FLOPs) less than 232.05M.

KW - 3D hand pose estimation

KW - Convolutional Neural Network

KW - Hand articulation

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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