Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2184-2191.DOI: 10.11772/j.issn.1001-9081.2021071319

• Multimedia computing and computer simulation • Previous Articles    

Point cloud registration algorithm based on residual attention mechanism

Tingwei QIN1,2, Pengcheng ZHAO1,2, Pinle QIN1,2(), Jianchao ZENG1,2, Rui CHAI1,2, Yongqi HUANG1,2   

  1. 1.Shanxi Medical Imaging and Data Analysis Engineering Research Center (North University of China),Taiyuan Shanxi 030051,China
    2.College of Data Science and Technology,North University of China,Taiyuan Shanxi 030051,China
  • Received:2021-07-22 Revised:2021-10-13 Accepted:2021-10-18 Online:2021-11-01 Published:2022-07-10
  • Contact: Pinle QIN
  • About author:QIN Tingwei, born in 1997, M. S. candidate. His research interests include point cloud registration, machine learning.
    ZHAO Pengcheng, born in 1995, M. S. His research interests include 3D point cloud processing, computer vision.
    ZENG Jianchao, born in 1963, Ph. D., professor. His research interests include maintenance decision and health management of complex system.
    CHAI Rui, born in 1985, Ph. D., lecturer. His research interests include medical image processing.
    HUANG Yongqi, born in 1997, M. S. candidate. His research interests include point cloud registration, computer vision.
  • Supported by:
    Shanxi Provincial Key Research and Development Plan(201803D31212-1);Construction Project of Engineering Technology Research Center of Shanxi Province(201805D121008)


秦庭威1,2, 赵鹏程1,2, 秦品乐1,2(), 曾建朝1,2, 柴锐1,2, 黄永琦1,2   

  1. 1.山西省医学影像人工智能工程技术研究中心(中北大学),太原 030051
    2.中北大学 大数据学院,太原 030051
  • 通讯作者: 秦品乐
  • 作者简介:秦庭威(1997—),男,陕西渭南人,硕士研究生,CCF会员,主要研究方向:点云配准、机器学习
  • 基金资助:


Aiming at the problems of low accuracy and poor robustness of traditional point cloud registration algorithms and the inability of accurate radiotherapy for cancer patients before and after radiotherapy, an Attention Dynamic Graph Convolutional Neural Network Lucas-Kanade (ADGCNNLK) was proposed. Firstly, residual attention mechanism was added to Dynamic Graph Convolutional Neural Network (DGCNN) to effectively utilize spatial information of point cloud and reduce information loss. Then, the DGCNN added with residual attention mechanism was used to extract point cloud features, this process was not only able to capture the local geometric features of the point cloud while maintaining the invariance of the point cloud replacement, but also able to semantically aggregate the information, thereby improving the registration efficiency. Finally, the extracted feature points were mapped to a high-dimensional space, and the classic image iterative registration algorithm LK (Lucas-Kanade) was used for registration of the nodes. Experimental results show that compared with Iterative Closest Point (ICP), Globally optimal ICP (Go-ICP) and PointNetLK, the proposed algorithm has the best registration effect with or without noise. Among them, in the case without noise, compared with PointNetLK, the proposed algorithm has the rotation mean squared error reduced by 74.61%, and the translation mean squared error reduced by 47.50%; in the case with noise, compared with PointNetLK, the proposed algorithm has the rotation mean squared error reduced by 73.13%, and the translational mean squared error reduced by 44.18%, indicating that the proposed algorithm is more robust than PointNetLK. And the proposed algorithm is applied to the registration of human point cloud models of cancer patients before and after radiotherapy, assisting doctors in treatment, and realizing precise radiotherapy.

Key words: point cloud registration, feature extraction, residual attention mechanism, deep learning, radiotherapy



关键词: 点云配准, 特征提取, 残差注意力机制, 深度学习, 放疗

CLC Number: