Show simple item record

dc.contributor.author
Wu, Hangbin
dc.contributor.author
Yang, Huimin
dc.contributor.author
Huang, Shengyu
dc.contributor.author
Zeng, Doudou
dc.contributor.author
Liu, Chun
dc.contributor.author
Zhang, Hao
dc.contributor.author
Guo, Chi
dc.contributor.author
Chen, Long
dc.date.accessioned
2020-08-03T12:12:08Z
dc.date.available
2020-08-03T02:48:49Z
dc.date.available
2020-08-03T12:12:08Z
dc.date.issued
2020-07-02
dc.identifier.issn
2072-4292
dc.identifier.other
10.3390/rs12142181
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/429610
dc.identifier.doi
10.3929/ethz-b-000429610
dc.description.abstract
The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89-19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Few labeled samples
en_US
dc.subject
Point clouds
en_US
dc.subject
Classification
en_US
dc.subject
Indoor scenario
en_US
dc.title
Classification of point clouds for indoor components using few labeled samples
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-07-08
ethz.journal.title
Remote Sensing
ethz.journal.volume
12
en_US
ethz.journal.issue
14
en_US
ethz.journal.abbreviated
Remote Sens.
ethz.pages.start
2181
en_US
ethz.size
22 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2020-08-03T02:48:54Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-08-03T12:12:25Z
ethz.rosetta.lastUpdated
2022-03-29T02:44:33Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Classification%20of%20point%20clouds%20for%20indoor%20components%20using%20few%20labeled%20samples&rft.jtitle=Remote%20Sensing&rft.date=2020-07-02&rft.volume=12&rft.issue=14&rft.spage=2181&rft.issn=2072-4292&rft.au=Wu,%20Hangbin&Yang,%20Huimin&Huang,%20Shengyu&Zeng,%20Doudou&Liu,%20Chun&rft.genre=article&rft_id=info:doi/10.3390/rs12142181&
 Search print copy at ETH Library

Files in this item

Thumbnail

Publication type

Show simple item record