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

A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
https://github.com/WeikaiTan/Toronto-3D

3d dataset deep-learning lidar point-cloud-dataset point-cloud-segmentation point-clouds semanticsegmentation

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A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

Host: GitHub
URL: https://github.com/WeikaiTan/Toronto-3D
Owner: WeikaiTan
Created: 2020-03-21T22:11:16.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2024-06-01T04:14:30.000Z (7 months ago)
Last Synced: 2024-08-03T14:04:35.978Z (5 months ago)
Topics: 3d, dataset, deep-learning, lidar, point-cloud-dataset, point-cloud-segmentation, point-clouds, semanticsegmentation
Homepage:
Size: 6.49 MB
Stars: 219
Watchers: 8
Forks: 31
Open Issues: 9
Metadata Files:
- Readme: README.md

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Awesome-3D-LiDAR-Datasets - Toronto-3D

README

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

[**[Paper]**](https://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Tan_Toronto-3D_A_Large-Scale_Mobile_LiDAR_Dataset_for_Semantic_Segmentation_of_CVPRW_2020_paper.html) [**[Download]**](#download) [**[Results]**](#results) [**[Codes]**](#code)

![Image](Screenshots/Sample_RGB.png)

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. Here is an overview of the dataset and the tiles. The approximate location of the dataset is at [(43.726, -79.417)](https://goo.gl/maps/g6mXjzVfw9pKbL546).

![Image](Screenshots/Overview.png)

Point clouds has [10 attributes](#attributes) and classified in [8 labelled object classes](#classes). There is a data preparation [tip](#tip) to handle UTM coordinates to avoid problems. There are also some [known issues](#issues).

Details on the dataset can be found at [CVPRW2020](http://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Tan_Toronto-3D_A_Large-Scale_Mobile_LiDAR_Dataset_for_Semantic_Segmentation_of_CVPRW_2020_paper.html). Revisions on the labels will lead to different results from the published paper, and updated results will be updated [here](#results).

If you have questions, or any suggestions to help us improve the dataset, please contact [Weikai Tan](mailto:[email protected]).

---

##  Semantic segmentation results (%)

More results to be added

*Default: point coordinates only*

| Method          | OA     | mIoU   | Road   | Road mrk. | Natural | Bldg | Util. line | Pole   | Car    | Fence  |

|------------------|--------|--------|--------|----------|---------|----------|-----------|--------|--------|--------|

| [PointNet++](https://github.com/charlesq34/pointnet2/blob/42926632a3c33461aebfbee2d829098b30a23aaa/models/pointnet2_sem_seg.py#L18)       | 84.88 | 41.81 | 89.27 | 0.00    | 69.0 | 54.1 | 43.7 | 23.3 | 52.0 | 3.0  |

| [PointNet++ MSG](https://github.com/charlesq34/pointnet2/blob/42926632a3c33461aebfbee2d829098b30a23aaa/models/pointnet2_cls_msg.py#L17) | 92.56 | 59.47 | 92.90 | 0.00    | 86.13  | 82.15   | 60.96    | 62.81 | 76.41 | 14.43 |

| PointNet++ *     | 91.66 | 58.01 | 92.71 | 7.68    | 84.30  | 81.83   | 67.44    | 63.30 | 60.92 | 5.92  |

| [DGCNN](https://github.com/WangYueFt/dgcnn/blob/20fdb459ca5d10fe8aba1d296e66340f65990b85/tensorflow/sem_seg/model.py#L20)  | 94.24 | 61.79 | 93.88 | 0.00 | 91.25 | 80.39 | 62.40 | 62.32 | 88.26 | 15.81 |

| [KPFCNN](https://github.com/HuguesTHOMAS/KPConv/blob/132fdc628fb4850548e931c8b02c6325e7cac85e/training_NPM3D.py#L49)           | 95.39 | 69.11 | 94.62 | 0.06    | 96.07  | 91.51   | 87.68    | 81.56 | 85.66 | 15.72 |

| [MS-PCNN](https://doi.org/10.1109/TITS.2019.2961060) | 90.03 | 65.89 | 93.84 | 3.83 | 93.46 | 82.59 | 67.80 | 71.95 | 91.12 | 22.50 |

| [TGNet](https://doi.org/10.1109/TGRS.2019.2958517) | 94.08 | 61.34 | 93.54 | 0.00    | 90.83  | 81.57   | 65.26    | 62.98 | 88.73 | 7.85  |

| [MS-TGNet](https://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Tan_Toronto-3D_A_Large-Scale_Mobile_LiDAR_Dataset_for_Semantic_Segmentation_of_CVPRW_2020_paper.html)  | 95.71 | 70.50 | 94.41 | 17.19   | 95.72  | 88.83   | 76.01    | 73.97 | 94.24 | 23.64 |

| RandLA-Net [(Hu, et al., 2021)](https://doi.org/10.1109/TPAMI.2021.3083288) | 92.95 | 77.71 | 94.61 | 42.62 | 96.89 | 93.01 | 86.51 | 78.07 | 92.85 | 37.12 |

| [Rim et al., 2021](https://doi.org/10.3390/rs13163121) | 72.55 | 66.87 | 92.74 | 14.75 | 88.66 | 93.52 | 81.03 | 67.71 | 39.65 | 56.90 |

| MappingConvSeg [(Yan, et al., 2021)](https://doi.org/10.1109/LGRS.2021.3107006) | 93.17 | 77.57 | 95.02 | 39.27 | 96.77 | 93.32 | 86.37 | 79.11 | 89.81 | 40.89 |

| DiffConv [(Lin & Feragen, 2022)](https://doi.org/10.1007/978-3-031-20062-5_22) | - | 76.73 | 83.31 | 51.06 | 69.04 | 79.55 | 80.48 | 84.41 | 76.19 | 89.83 |

| EyeNet [(Yoo et al., 2023)](https://doi.org/10.1109/CVPRW59228.2023.00699) | 94.63 | 81.13 | 96.98 | 65.02 | 97.83 | 93.51 | 86.77 | 84.86 | 94.02 | 30.01 |

| LACV-Net [(Zeng et al., 2024)](https://doi.org/10.1016/j.eswa.2024.123269) | 95.8 | 78.5 | 94.8 | 42.7 | 96.7 | 91.4 | 88.2 | 79.6 | 93.9 | 40.6 |

| DCTNet [(Lu et al., 2024)](https://doi.org/10.1016/j.jag.2024.103791) | - | 81.84 | 82.77 | 59.53 | 85.51 | 86.47 | 81.79 | 84.03 | 79.55 | 96.21 |

| **Use RGB**

| RandLA-Net [(Hu, et al., 2021)](https://doi.org/10.1109/TPAMI.2021.3083288) (RGB)| 94.37 | 81.77 | 96.69 | 64.21 | 96.92 | 94.24 | 88.06 | 77.84 | 93.37 | 42.86 |

| [Rim et al., 2021](https://doi.org/10.3390/rs13163121) (RGB) | 83.60 | 71.03 | 92.84 | 27.43 | 89.90 | 95.27 | 85.59 | 74.50 | 44.41 | 58.30 |

| MappingConvSeg [(Yan, et al., 2021)](https://doi.org/10.1109/LGRS.2021.3107006) | 94.72 | 82.89 | 97.15 | 67.87 | 97.55 | 93.75 | 86.88 | 82.12 | 93.72 | 44.11 |

| ResDLPS-Net [(Du et al., 2021)](https://doi.org/10.1016/j.isprsjprs.2021.09.024) | 96.49 | 80.27 | 95.82 | 59.80 | 96.10 | 90.96 | 86.82 | 79.95 | 89.41 | 43.31 |

| LACV-Net [(Zeng et al., 2024)](https://doi.org/10.1016/j.eswa.2024.123269) | 97.4 | 82.7 | 97.1 | 66.9 | 97.3 | 93.0 | 87.3 | 83.4 | 93.4 | 43.1 |

| **Others**

| [Han et al., 2021](https://doi.org/10.1016/j.isprsjprs.2021.03.001) (Intensity + Normal) | 93.60 | 70.80 | 92.20 | 53.80 | 92.80 | 86.00 | 72.20 | 72.50 | 75.70 | 21.20 |

*\* use same radii and k as TGNet*

---

##  Codes for training your own network

* [Code for RandLA-Net](https://github.com/WeikaiTan/RandLA-Net.git)

* [Code for KPFCNN](https://github.com/HuguesTHOMAS/KPConv-PyTorch) updated thanks to @Yarroudh

---

##  Point cloud attributes 

* XYZ

* RGB

* Intensity

* GPS time

* Scan angle rank

##  Classes 

* Road (label 1) 

* Road marking (label 2)

* Natural (label 3)

* Building (label 4)

* Utility line (label 5)

* Pole (label 6)

* Car (label 7)

* Fence (label 8)

* unclassified (label 0)

---

##  Data preparation tip

The XY coordinates are stored in UTM format. The Y coordinate may exceed decimal digits in `float` type commonly used in point cloud processing algorithms. Directly read and process the coordinates could result in loss of detail and wrong geometric features.

I set a `UTM_OFFSET = [627285, 4841948, 0]` to subtract from the raw coordinates. You may use any other numbers to reduce number of digits.

Example of potential issues during `grid_subsampling` operation used in KPConv and RandLA-Net: both subsampled to grid size 6cm

| without offset | with offset |

|:--------------:|:-----------:|

| ![](Screenshots/without_offset.png) | ![](Screenshots/with_offset.png) |

---

##  Known issues 

1. Point RGB assignments on taller vehicles.

![Image](Screenshots/Issue_1.png)

2. Point RGB artifact assignments on moving vehicles.

![Image](Screenshots/Issue_2.png)

3. Point acquisition on moving vehicles.

![Image](Screenshots/Issue_3.png)

---

##  Download

Dataset can be downloaded at [OneDrive](https://1drv.ms/u/s!Amlc6yZnF87psX6hKS8VOQllVvj4?e=yWhrYX) or [百度网盘](https://pan.baidu.com/s/16FVZqPU-I56rFRrGWoaxXA)(提取码：aewp).

Check [Changelog](#changelog) for changes.

Toronto-3D belongs to [Mobile Sensing and Geodata Science Lab](https://uwaterloo.ca/mobile-sensing/), University of Waterloo. Toronto-3D is distributed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) License

## 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}

    }

## Acknowledgements

Teledyne Optech is acknowledged for providing mobile LiDAR point cloud data collected by [Maverick](https://www.teledyneoptech.com/en/products/mobile-survey/maverick/). Thanks Jing Du and Dr. Guorong Cai from Jimei University for point cloud labelling.

Thanks Intel ISL for including our dataset in the [Open3D-ML](https://github.com/intel-isl/Open3D-ML) 3D Machine Learning module.

---

##  Changelog 

* [2023-02-07] Added code for RandLA-Net

* [2020-04-23] Uploaded newest version. Fixed some labelling errors. Major revision on cars.

* [2020-03-22] Uploaded newest version.