Steerable Graph Neural Network on Point Clouds via Second-Order Random Walks

Xianglin GUO, Yifan WANG, Heng LIU, Haoran XIE, Gary CHENG, Fu Lee WANG

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


Point cloud analysis, arising from computer graphics, remains a fundamental but challenging problem, mainly due to the non-Euclidean property of point cloud data modality. With the snap increase in the amount and breadth of related research in deep learning for graphs, many important works come in the form of graphs representing the point clouds. In this paper, we present a sampling adaptive graph convolutional network that combines the powerful representation ability of random walk subgraph searching and the essential success of the Fisher vector. Extending from those existing graph representation learning or embedding methods with multi-hop neighbor random searching, we sample multi-scale walk fields by using a steerable exploration-exploitation second order random walk , which endows our model with the most flexibility compared with the original first order random walk. To encode each-scale walk field consisting of several walk paths, specifically, we characterize these paths of walk field by Gaussian mixture models (GMMs) so as to better analogize the standard CNNs on Euclidean modality. Each Gaussian component implicitly defines a direction and all of them properly encode the spatial layout of walk fields after the gradient projecting to the space of Gaussian parameters, i.e. the Fisher vectors. Thereby, we introduce and name our deep graph convolutional network as PointFisher . Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed learning method over other state-of-the-arts for point cloud classification and segmentation.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Multimedia
Early online date6 Nov 2023
Publication statusE-pub ahead of print - 6 Nov 2023

Bibliographical note

Publisher Copyright:


  • Convolution
  • Convolutional neural networks
  • Feature extraction
  • Fisher vectors
  • Gaussian mixture model
  • Gaussian mixture models
  • Point cloud compression
  • Second order random walk
  • Task analysis
  • Three-dimensional displays
  • steerable graph neural network


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