Toronto-3D

Dataset Information
Modalities
Point cloud
License
Unknown
Homepage

Overview

Toronto-3D is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points. Point clouds has 10 attributes and classified in 8 labelled object classes.

Source: https://github.com/WeikaiTan/Toronto-3D
Image Source: https://github.com/WeikaiTan/Toronto-3D

Variants: Toronto-3D, Toronto-3D L002

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

Task Model Paper Date
3D Semantic Segmentation KPFCNN Toronto-3D: A Large-scale Mobile LiDAR … 2020-03-18
3D Semantic Segmentation TGNet Toronto-3D: A Large-scale Mobile LiDAR … 2020-03-18
3D Semantic Segmentation MS-PCNN Toronto-3D: A Large-scale Mobile LiDAR … 2020-03-18
3D Semantic Segmentation PointNet++ Toronto-3D: A Large-scale Mobile LiDAR … 2020-03-18
3D Semantic Segmentation DGCNN Toronto-3D: A Large-scale Mobile LiDAR … 2020-03-18
3D Semantic Segmentation RandLANet RandLA-Net: Efficient Semantic Segmentation of … 2019-11-25

Research Papers

Recent papers with results on this dataset: