TY - GEN
T1 - Toronto-3D
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
AU - Tan, Weikai
AU - Qin, Nannan
AU - Ma, Lingfei
AU - Li, Ying
AU - Du, Jing
AU - Cai, Guorong
AU - Yang, Ke
AU - Li, Jonathan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released 1 to encourage new research, and the labels will be improved and updated with feedback from the research community.
AB - Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released 1 to encourage new research, and the labels will be improved and updated with feedback from the research community.
UR - http://www.scopus.com/inward/record.url?scp=85090111295&partnerID=8YFLogxK
U2 - 10.1109/CVPRW50498.2020.00109
DO - 10.1109/CVPRW50498.2020.00109
M3 - Conference contribution
AN - SCOPUS:85090111295
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 797
EP - 806
BT - Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PB - IEEE Computer Society
Y2 - 14 June 2020 through 19 June 2020
ER -