DublinCity: Annotated LiDAR Point Cloud and its Applications

Aljosa Smolic 1 smolica@scss.tcd.ie 1 V-SENSE School of Computer Science and Statistics T rinity College Dublin, Ireland 2 University of Houston-Victoria, Victoria, T exas, US 3 CAAD Department of Architecture ETH, Zurich, Switzerland 4 Department of Architecture T arbiat Modares University T ehran, Iran 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.) 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. Secondly, the complete ALS dataset is applied as detailed ground truth for city-scale image-based 3D reconstruction. 1 Introduction In computer vision, automated identification of three-dimensional (3D) assets in an unstructured large dataset is essential for scene understanding.