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Lookup NU author(s): Dr Wen Xiao
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Springer Nature, 2022.
For re-use rights please refer to the publisher's terms and conditions.
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk/.
Author(s): Hu Q, Yang B, Khalid S, Xiao W, Trigoni N, Markham A
Publication type: Article
Publication status: Published
Journal: International Journal of Computer Vision
Year: 2022
Volume: 130
Pages: 316-343
Print publication date: 01/02/2022
Online publication date: 04/01/2022
Acceptance date: 11/11/2021
Date deposited: 06/02/2022
ISSN (print): 0920-5691
ISSN (electronic): 1573-1405
Publisher: Springer Nature
URL: https://doi.org/10.1007/s11263-021-01554-9
DOI: 10.1007/s11263-021-01554-9
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