SynthCity: A large scale synthetic point cloud

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SynthCity: A large scale synthetic point cloud

Griffiths, D; Boehm, J; (2019) SynthCity: A large scale synthetic point cloud. ArXiv: Ithaca, NY, USA. Green open access

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Abstract

With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist which, if proved effective can be exploited. We therefore argue that research in this domain would be of significant use. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is assigned a label from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.

Type:	Working / discussion paper
Title:	SynthCity: A large scale synthetic point cloud
Open access status:	An open access version is available from UCL Discovery
Publisher version:	https://doi.org/10.48550/arXiv.1907.04758
Language:	English
Additional information:	This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10132084