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.

本文介绍了一种用于城市规模高密度航空激光扫描（ALS）点云数据的标注数据集，并将其用于训练卷积神经网络（CNNs）分类城市元素和用于城市规模基于图像的 3D 建模的性能验证。