Toronto-3D is a large-scale urban outdoor point cloud dataset for semantic segmentation. This large-scale labeled dataset is acquired by the Toronto MLS system in Canada, covers approximately 1 km of point cloud, and consists of approximately 78.3 million points with 8 labeled object classes.