3D point cloud analysis and classification in large-scale scene based on deep learning

Lei Wang, Weiliang Meng, Runping Xi, Yanning Zhang, Chengcheng Ma, Ling Lu, Xiaopeng Zhang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

We present a deep learning framework for efficient large-scale 3D point cloud analysis and classification using the designed feature description matrix (FDM). As the 3D points are unordered in the large-scale scene, and no topology structure can be employed directly for classification and recognition, it is difficult to apply deep neural network directly on 3D point clouds as points cannot be arranged in a fixed order as 2D image pixels. We design a new pipeline for 3D data processing by combining the traditional features extraction method and deep learning method. Our FDM encapsulates the 3D features of the point and can be used as the input of the deep neural network for training and testing. The experiments demonstrate that our method can acquire higher classification accuracy compared with our previous work and other state-of-art works.

Original languageEnglish
Article number8684197
Pages (from-to)55649-55658
Number of pages10
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • CNN
  • feature description matrix
  • geometric features
  • point cloud

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