SynthCity: A large scale synthetic point cloud

Abstract

With deep learning becoming a more prominent approach for the 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 proven effective can be exploited. We, therefore, argue that research in this domain would be of significant use. In this work, 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.

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