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.

Citation

Please consider citing our work: @inproceedings{tan2020toronto3d, title={{Toronto-3D}: A large-scale mobile lidar dataset for semantic segmentation of urban roadways}, author={Tan, Weikai and Qin, Nannan and Ma, Lingfei and Li, Ying and Du, Jing and Cai, Guorong and Yang, Ke and Li, Jonathan}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops}, pages={202–203}, year={2020} }