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

随着深度学习在自动分类三维点云数据方面的地位日益凸显，一个关键的瓶颈在于高质量训练数据量的不足，尤其是与二维图像所拥有的数据量相比。一种潜在的解决方案是利用合成数据进行网络预训练，然而，对于点云而言，模型从合成数据泛化到真实世界数据的能力尚未得到充分研究。尽管如此，大量三维虚拟环境的存在，若证明其有效性，则可加以充分利用。因此，我们认为在这一领域的研究将极具价值。在本论文中，我们提出了SynthCity，一个开源数据集，旨在助力研究。SynthCity是一个包含367.9M个点、全彩色的合成移动激光扫描点云数据集。每个点都被标记为九个类别之一。我们利用Blender中的Blensor插件在典型的城市/郊区环境中生成我们的点云。更多信息请参阅我们的项目网站http://www.synthcity.xyz或论文https://arxiv.org/abs/1907.04758。