DublinCity: Annotated LiDAR Point Cloud and its Applications

TL;DR

This paper introduces the first densely annotated city-scale LiDAR dataset for Dublin, enabling improved urban scene understanding through CNN training and 3D reconstruction validation.

Contribution

It provides a novel, densely labeled ALS point cloud dataset with hierarchical annotations for urban elements at city scale.

Findings

CNNs trained on the dataset achieve high classification accuracy.

The dataset improves 3D city model reconstructions.

Benchmark results demonstrate dataset's utility for urban scene analysis.

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.