Show simple item record

dc.contributor.authorAgapaki, Evangeliaen
dc.contributor.authorGlyn-Davies, Alexen
dc.contributor.authorMandoki, Saraen
dc.contributor.authorBrilakis, Ioannisen
dc.date.accessioned2019-02-13T00:31:34Z
dc.date.available2019-02-13T00:31:34Z
dc.date.issued2019en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/289351
dc.description.abstractGeneration of digital models of existing industrial facilities is labor intensive and expensive. The use of state-of-the-art deep learning algorithms can assist to reduce the modelling time and cost. However large databases of labelled, laser-scanned industrial facilities do not exist to date, henceforth training of deep learning models is not possible. Our paper solves this problem by proposing a new benchmark dataset, which consists of five labelled industrial plants. The labelling schema that we followed for the generation of this dataset is based on the frequency of appearance of industrial object types. We labelled the ten most frequent industrial object shapes as identified in previous work. We present CLOI (Channels, L-shapes, circular sections, I-shapes): a richly annotated large-scale repository of shapes represented by labelled point clusters. CLOI has more than 140 million hand labelled points and serves as the foundation for researchers who are interested in automated modelling of industrial assets using deep learning algorithms.
dc.titleCLOI: A Shape Classification Benchmark Dataset for Industrial Facilitiesen
dc.typeConference Object
prism.endingPage73
prism.publicationDate2019en
prism.publicationNameCOMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCEen
prism.startingPage66
dc.identifier.doi10.17863/CAM.36600
dcterms.dateAccepted2019-01-11en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019en
dc.contributor.orcidAgapaki, Evangelia [0000-0002-2962-9203]
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
rioxxterms.typeConference Paper/Proceeding/Abstracten
pubs.funder-project-idLeverhulme Trust (IAF-2018-011)
pubs.funder-project-idEPSRC (EP/I019308/1)
pubs.funder-project-idEPSRC (EP/K000314/1)
pubs.funder-project-idEPSRC (EP/L010917/1)
pubs.funder-project-idEPSRC (EP/N021614/1)
cam.orpheus.successMon Feb 15 07:36:12 GMT 2021 - Embargo updated*
cam.orpheus.counter17*
rioxxterms.freetoread.startdate2020-12-31


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record