Point Cloud Registration Using Multi-Attention Mechanism and Deep Hybrid Features

Yu Xin Zhang, Zhan Li Sun, Zhi Gang Zeng, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Due to some unfavorable factors, how to accurately register point clouds is still a challenging task. In this paper, an effective point cloud registration network is proposed with multiple attention mechanism and deep hybrid features. For the features obtained with a Graph Neural Network (GNN), three attention modules, namely the spatial attention module, channel attention module, and self-geometric attention module, are utilized to mine various areas of regional information. An attention-based feature fusion module, which consists of three consecutive residual blocks, is devised to fuse the features from the three attention modules. Moreover, the capability of the network for correctly matching point clouds is enhanced, by using deep hybrid features to guide the correspondence search and the calculation of matching confidence. Experimental results on several widely used data sets demonstrate the effectiveness of the proposed point cloud registration network.

Original languageEnglish
Article number9942317
Pages (from-to)1-10
Number of pages10
JournalIEEE Intelligent Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Convolution
  • deep hybrid feature
  • Feature extraction
  • Intelligent systems
  • multi-attention mechanism
  • Point cloud compression
  • point cloud registration
  • Sun
  • Task analysis
  • Three-dimensional displays

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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