SynthCity Dataset - Complete

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. See our project website http://www.synthcity.xyz or paper https://arxiv.org/abs/1907.04758 for more information.

随着深度学习成为三维点云数据（three-dimensional point cloud data）自动分类的主流方法，其核心瓶颈之一在于高质量训练数据的体量——相较于二维图像领域可获取的训练数据，这一差距尤为显著。一种潜在的解决方案是利用合成数据对神经网络进行预训练，但目前针对点云领域，模型从合成数据泛化至真实世界数据的能力尚未得到充分研究。尽管如此，现有的三维虚拟环境资源极为丰富，若能证明其有效性，便可加以开发利用。因此我们认为，该领域的研究具备极高的实用价值。本文提出SynthCity这一开源数据集以助力相关研究。SynthCity是一个包含367.9M个点的全彩色移动激光扫描（Mobile Laser Scanning）点云数据集。每个点均被标注为九大类别的其中一类。我们通过Blender的Blensor插件，在典型的城市/郊区环境中生成该点云。欲了解更多信息，请访问我们的项目网站http://www.synthcity.xyz 或相关论文https://arxiv.org/abs/1907.04758。