GHMM: Learning Generative Hybrid Mixture Models for Generalized Point Set Registration in Computer-Assisted Orthopedic Surgery-Reference-Cited by-同舟云学术

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GHMM: Learning Generative Hybrid Mixture Models for Generalized Point Set Registration in Computer-Assisted Orthopedic Surgery

Published:2024-08 Issue:3 Volume:6 Page:1285-1295
ISSN:2576-3202
Container-title:IEEE Transactions on Medical Robotics and Bionics
language:
Short-container-title:IEEE Trans. Med. Robot. Bionics

Author:

Zhang Zhengyan¹^ORCID,Zhang Ang²^ORCID,Lai Jiewen³^ORCID,Ren Hongliang³^ORCID,Song Rui⁴^ORCID,Li Yibin⁴^ORCID,Meng Max Q.-H.³^ORCID,Min Zhe⁴^ORCID

Affiliation:

1. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China

2. Research and Development Department, Yuanhua Robotics, Perception and AI Technologies Ltd., Shenzhen, China

3. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China

4. School of Control Science and Engineering, Shandong University, Jinan, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8253409/10631864/10542369.pdf?arnumber=10542369

Reference47 articles.

1. A Generalized Full-to-Partial Registration Framework of 3D Point Sets for Computer-Aided Orthopedic Surgery

2. Surgical Robotics and Computer-Integrated Interventional Medicine [Scanning the Issue]

3. 3-D Rigid Point Set Registration for Computer-Assisted Orthopedic Surgery (CAOS): A Review From the Algorithmic Perspective

4. Aligning 3D Curve With Surface Using Tangent and Normal Vectors for Computer-Assisted Orthopedic Surgery

5. Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration

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