Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

@article{Tan2020Toronto3DAL,
  title={Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways},
  author={Weikai Tan and Nannan Qin and L. Ma and Y. Li and Jing Du and Guorong Cai and K. Yang and J. Li},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2020},
  pages={797-806}
}
  • Weikai Tan, Nannan Qin, +5 authors J. Li
  • Published 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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… Expand
12 Citations
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels
  • Highly Influenced
  • PDF
Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
  • Highly Influenced
  • PDF
TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas
  • 5
  • Highly Influenced
  • PDF
3D Semantic Scene Completion: a Survey
  • PDF
A point-based deep learning network for semantic segmentation of MLS point clouds
  • Xu Han, Zhen Dong, Bisheng Yang
  • Computer Science
  • 2021
SUM: A Benchmark Dataset of Semantic Urban Meshes
  • 1
  • PDF
Deep Learning for 3D Point Clouds: A Survey
  • 132
  • PDF
Road Information Extraction from Mobile LiDAR Point Clouds using Deep Neural Networks
  • L. Ma
  • Computer Science
  • 2020
...
1
2
...

References

SHOWING 1-10 OF 37 REFERENCES
Dynamic Graph CNN for Learning on Point Clouds
  • 1,150
  • Highly Influential
  • PDF
Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification
  • 77
  • Highly Influential
  • PDF
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
  • 2,520
  • Highly Influential
  • PDF
Contextual classification with functional Max-Margin Markov Networks
  • 131
  • Highly Influential
  • PDF
Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object Segmentation From LiDAR Point Clouds in Large-Scale Environments
  • 7
  • PDF
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
  • Q. Hu, Bo Yang, +5 authors A. Markham
  • Computer Science, Engineering
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
  • 140
  • PDF
TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation
  • 13
  • PDF
KPConv: Flexible and Deformable Convolution for Point Clouds
  • 324
  • PDF
PointConv: Deep Convolutional Networks on 3D Point Clouds
  • 291
  • PDF
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
  • 196
  • PDF
...
1
2
3
4
...