Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence
Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence
About this item
Full title
Author / Creator
Zhang, Zhengyan , Lyu, Erli , Min, Zhe , Zhang, Ang , Yu, Yue and Meng, Max Q.-H.
Publisher
Basel: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method for accurately estimating point correspondences and handling large transformations using limited pr...
Alternative Titles
Full title
Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_689e7dcf731f49e8a0f6c9a1e1afc854
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_689e7dcf731f49e8a0f6c9a1e1afc854
Other Identifiers
ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs15184493
