dc.contributor.author | Smolic, Aljosa | |
dc.contributor.author | Zolanvari, S.M. Iman | |
dc.contributor.author | Ruano, Susana | |
dc.contributor.author | Rana, Aakanksha | |
dc.contributor.author | Cumins, Alan | |
dc.contributor.author | da Silva, Rogerio Eduardo | |
dc.contributor.author | Rahbar, Morteza | |
dc.date.accessioned | 2020-02-18T17:11:36Z | |
dc.date.available | 2020-02-18T17:11:36Z | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019 | en |
dc.identifier.citation | Zolanvari, S.M.I., Ruano, S., Rana, A., Cummins, A., da Silva, R.E., Rahbar, M. & Smolic, A., DublinCity: Annotated LiDAR Point Cloud and its Applications, BMVC 30th British Machine Vision Conference, Forthcoming, 2019 | en |
dc.identifier.other | Y | |
dc.description | PUBLISHED | en |
dc.description.abstract | Scene understanding of full-scale 3D models of an urban area remains a challenging
task. While advanced computer vision techniques offer cost-effective approaches to
analyse 3D urban elements, a precise and densely labelled dataset is quintessential. The
paper presents the first-ever labelled dataset for a highly dense Aerial Laser Scanning
(ALS) point cloud at city-scale. This work introduces a novel benchmark dataset that
includes a manually annotated point cloud for over 260 million laser scanning points
into 100’000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects
are labelled into 13 classes using hierarchical levels of detail from large (i.e. building,
vegetation and ground) to refined (i.e. window, door and tree) elements. To validate the
performance of our dataset, two different applications are showcased. Firstly, the labelled
point cloud is employed for training Convolutional Neural Networks (CNNs) to
classify urban elements. The dataset is tested on the well-known state-of-the-art CNNs
(i.e. PointNet, PointNet++ and So-Net). Secondly, the complete ALS dataset is applied
as detailed ground truth for city-scale image-based 3D reconstruction. | en |
dc.language.iso | en | en |
dc.rights | Y | en |
dc.subject | 3D models | en |
dc.subject | Aerial Laser Scanning | en |
dc.subject | LiDAR Point Cloud | en |
dc.subject | Urban areas | en |
dc.title | DublinCity: Annotated LiDAR Point Cloud and its Applications | en |
dc.title.alternative | BMVC 30th British Machine Vision Conference, Forthcoming. | en |
dc.type | Conference Paper | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/smolica | |
dc.identifier.rssinternalid | 212576 | |
dc.rights.ecaccessrights | openAccess | |
dc.subject.TCDTheme | Creative Technologies | en |
dc.subject.TCDTag | Multimedia & Creativity | en |
dc.subject.darat_impairment | Other | en |
dc.status.accessible | N | en |
dc.contributor.sponsor | SFI stipend | en |
dc.contributor.sponsorGrantNumber | 15/RP/2776 | en |
dc.identifier.uri | https://v-sense.scss.tcd.ie/wp-content/uploads/2019/08/BMVC_2019_PointCloud__Copy_-2.pdf | |
dc.identifier.uri | http://hdl.handle.net/2262/91577 | |