Hierarchical Grow Network for Point Cloud Segmentation

Jiajing Chen, Burak Kakillioglu, Senem Velipasalar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a novel deep learning-based point cloud segmentation model, referred to as the hierarchical grow network, is proposed. This model is based on the PointNet segmentation model.Different from PointNet, the entire point cloud is divided into several clusters, which are then combined at different hierarchical levels. This way, information from different receptive fields is used to make the final segmentation. The proposed hierarchical growing approach can also be used with other 3D feature extraction networks. Experiments performed on the indoor S3DIS database show that our proposed approach provides improvement and higher accuracy compared to the PointNet model.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1558-1562
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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