DublinCity: Annotated LiDAR Point Cloud and its Applications

S M Iman Zolanvari (Trinity College Dublin), Susana Ruano (Trinity College Dublin), Aakanksha Rana (Trinity College Dublin), Alan Cummins (Trinity College Dublin), Rogério Eduardo da Silva (University of Houston-Victoria), Morteza Rahbar (CAAD, ITA, ETH Zurich), Aljosa Smolic (Trinity College Dublin)

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 (Laefer, et al) 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.


Paper (PDF)

title={DublinCity: Annotated LiDAR Point Cloud and its Applications},
author={S M Iman Zolanvari and Susana Ruano and Aakanksha Rana and Alan Cummins and Rogério Eduardo da Silva and Morteza Rahbar and Aljosa Smolic},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},