The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances.

DOI Link: 
10.21227/fvqd-k603
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[1] Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian, "PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/fvqd-k603. Accessed: Feb. 05, 2025.
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doi = {10.21227/fvqd-k603},
url = {http://dx.doi.org/10.21227/fvqd-k603},
author = {Muhammad Ibrahim; Naveed Akhtar; Michael Wise; Ajmal Mian },
publisher = {IEEE Dataport},
title = {PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation},
year = {2021} }
TY - DATA
T1 - PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation
AU - Muhammad Ibrahim; Naveed Akhtar; Michael Wise; Ajmal Mian
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PB - IEEE Dataport
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Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian. (2021). PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation. IEEE Dataport. http://dx.doi.org/10.21227/fvqd-k603
Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian, 2021. PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation. Available at: http://dx.doi.org/10.21227/fvqd-k603.
Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian. (2021). "PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation." Web.
1. Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian. PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/fvqd-k603
Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian. "PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation." doi: 10.21227/fvqd-k603