Exploiting Manifold Feature Representation for Efficient Classification of 3D Point Clouds

Dinghao YANG, Wei GAO, Ge LI, Hui YUAN, Junhui HOU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review


In this paper, we propose an efficient point cloud classification method via manifold learning based feature representation. Different from conventional methods, we use manifold learning algorithms to embed point cloud features for better considering the geometric continuity on the surface. Then, the nature of point cloud can be acquired in low dimensional space, and after being concatenated with features in the original three-dimensional (3D) space, both the capability of feature representation and the classification network performance can be improved. We explore three traditional manifold algorithms (i.e., Isomap, Locally-Linear Embedding, and Laplacian eigenmaps) in detail, and finally, we select the Locally-Linear Embedding (LLE) algorithm due to its low complexity and locality consistency preservation. Furthermore, we propose a neural network based manifold learning (NNML) method to implement manifold learning based non-linear projection. Experiments demonstrate that the proposed two manifold learning methods can obtain better performances than the state-of-the-art methods, and the obtained mean class accuracy (mA) and overall accuracy (oA) can reach 91.4% and 94.4%, respectively. Moreover, because of the improved feature learning capability, the proposed NNML method can also have better classification accuracy on models with prominent geometric shapes. To further demonstrate the advantages of PointManifold, we extend it as a plug and play method for point cloud classification task, which can be directly used with existing methods and gain a significant improvement.

Original languageEnglish
Article number50
JournalACM Transactions on Multimedia Computing, Communications and Applications
Issue number1s
Early online date23 Jan 2023
Publication statusPublished - 23 Jan 2023
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by The Major Key Project of PCL, Guangdong Basic and Applied Basic Research Foundation (2019A1515012031), Shenzhen Fundamental Research Program (GXWD20201231165807007-20200806163656003), Shenzhen Science and Technology Plan Basic Research Project (JCYJ20190808161805519), Natural Science Foundation of China (62031013), the Hong Kong GRF-RGC General Research Fund under Grant 11209819 (CityU 9042816) and Grant 11203820 (9042598).

Publisher Copyright:
© 2023 Association for Computing Machinery.


  • 3D vision
  • deep neural network
  • feature representation
  • manifold learning
  • Point cloud classification


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