SynthCity: A large scale synthetic point cloud

TL;DR

SynthCity introduces a large-scale synthetic colored point cloud dataset for urban environments, aiming to improve deep learning models' ability to generalize from synthetic to real-world 3D data.

Contribution

The paper presents SynthCity, a comprehensive synthetic point cloud dataset with detailed labels, facilitating research on pre-training and domain adaptation for 3D point cloud classification.

Findings

Provides a 367.9 million point synthetic dataset

Includes labeled points across nine categories

Supports research in synthetic-to-real domain transfer

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.