text: 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.
  @cite_15: 
  @cite_10: The objective of the TerraMobilita iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser scanning (MLS) at large scale. A very detailed semantic tree for urban scenes is proposed. We call analysis the capacity of a method to separate the points of the scene into these categories (classification), and to separate the different objects of the same type for object classes (detection). A very large ground truth is produced manually in two steps using advanced editing tools developed especially for this benchmark. Based on this ground truth, the benchmark aims at evaluating the classification, detection and segmentation quality of the submitted results. Graphical abstractDisplay Omitted HighlightsVery rich data: high accuracy, high resolution, many attributes.Massive data: 160 million annotated points thanks to a performant web based annotation tool (and many hours of work).Rich semantics organized in a semantic tree with various levels of generalization.Very objective evaluation metrics.
  @cite_8: This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to learn classification algorithm, however, given that a great attention has been paid to the split between the different objects, this dataset can also be used to learn the segmentation. The dataset consists of around 2km of MLS point cloud acquired in two cities. The number of points and range of classes make us consider that it can be used to train Deep-Learning methods. Besides we show some results of automatic segmentation and classification. The dataset is available at: http: caor-mines-paristech.fr fr paris-lille-3d-dataset .
summary: The need for outdoor labelled point clouds has been addressed by a range of researchers. , @cite_15 released the Paris-rue-Madame MLS dataset containing 20M points ( @math and reflectance), , @cite_10 the iQmulus dataset containing 300M points ( @math , time, reflectance and number of echoes) and , @cite_8 the Paris-Lille-3D containing 143.1M points ( @math , scanner @math , gps time, reflectance). However, many caveats exist within these datasets. For example, Paris-rue-Madame, whilst large enough for traditional machine learning algorithms (i.e. Support Vector Machines, Random Forest), does not meet the scale for a modern DNN, which number of parameters can easily exceed 10x the number of points available. The iQmulus is more suited in terms of size however due to a 2D semi-manual data labelling approach, many mislabelled ground truth points exist.