Show simple item record

dc.contributor.authorTrzeciak, Maciej
dc.contributor.authorBrilakis, Ioannis
dc.date.accessioned2022-06-01T23:30:05Z
dc.date.available2022-06-01T23:30:05Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337665
dc.description.abstractWe propose a mobile 3D reconstruction method for improving the precision and density of point clouds. It is suitable for hand-held scanners comprised of a colour camera and a lidar. We fuse time-synchronized and spatially registered images and lidar sweeps using deep learning techniques into dense scans, which are then used for progressive reconstruction in an odometry-like manner. We build a prototypic scanner and test our method in an indoor case-study. The results show that our pipeline outperforms reconstructions by other devices and methods, yielding relatively denser and detail-preserving point clouds with a 46% reduction in noise of reconstructed planar surfaces.
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titlePRECISE AND DENSE AI-BASED MOBILE 3D RECONSTRUCTION OF INDOOR SCENES BY CAMERA-LIDAR FUSION AND ODOMETRY
dc.typeConference Object
dc.publisher.departmentDepartment of Engineering Student
dc.date.updated2022-04-06T13:29:19Z
dc.identifier.doi10.17863/CAM.85071
dcterms.dateAccepted2022-03-19
rioxxterms.versionofrecord10.17863/CAM.85071
rioxxterms.versionAM
dc.contributor.orcidTrzeciak, Maciej [0000-0001-8188-487X]
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
pubs.conference-name2022 European Conference on Computing in Construction
pubs.conference-start-date2022-07-24
cam.orpheus.counter26*
cam.depositDate2022-04-06
pubs.conference-finish-date2022-06-26
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-06-01


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record