SynthCity Dataset - All Areas

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 hugely useful. 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 labelled from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.

随着深度学习逐渐成为三维点云数据自动分类的主流方法，其面临的一项核心瓶颈在于高质量训练数据的体量，尤其相较于二维图像所拥有的可用数据量而言。一种潜在的解决方案是利用合成数据对网络进行预训练，但针对点云领域，模型从合成数据泛化至真实世界数据的能力却鲜有研究。尽管如此，当前已存在大量三维虚拟环境，若能证明其有效性便可加以开发利用。因此我们认为，该领域的研究将极具实用价值。本文提出了一款助力相关研究的开源数据集SynthCity。SynthCity是一个包含3.679亿个点的全彩合成移动激光扫描点云数据集，每个点均被标注为九大类之一。我们借助Blender的Blensor插件，在典型的城市/郊区环境中生成了该点云数据集。