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 Jonathan 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 Jonathan 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
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References

SHOWING 1-10 OF 37 REFERENCES
Dynamic Graph CNN for Learning on Point Clouds
Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Contextual classification with functional Max-Margin Markov Networks
Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object Segmentation From LiDAR Point Clouds in Large-Scale Environments
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
TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation
KPConv: Flexible and Deformable Convolution for Point Clouds
PointConv: Deep Convolutional Networks on 3D Point Clouds
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
...
1
2
3
4
...