Data Preparation

Overall Structure

└── data 
    └── sets
         │── Robo3D      
         │   │── KITTI-C                
         │   │    │── beam_missing       
         │   │    │── crosstalk
         │   │    │── ...  
         │   │    └── wet_ground
         │   │── SemanticKITTI-C    
         │   │── nuScenes-C
         │   └── WOD-C                                                                                   
         │── KITTI          
         │── SemanticKITTI       
         │── nuScenes                
         └── WOD

KITTI

To install the KITTI dataset, download the data, annotations, and other files from https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d. Unpack the compressed file(s) into /data/sets/kitti and re-organize the data structure. Your folder structure should end up looking like this:

└── kitti  
    ├── ImageSets
    │       ├── train.txt
    │       └── val.txt
    ├── training
    │       └── calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
    └── testing
            └── calib & velodyne & image_2

KITTI-C

Download the KITTI-C dataset from the OpenDataLab platform and unpack it.

Direct download:
- Go to https://opendatalab.com/OpenDataLab/KITTI-C/tree/main/raw.
- Click the download button on the right to download the dataset.
CLI download:
- Install the openxlab package.
- ```
pip install openxlab
```
- Use the following command to download the dataset.
- ```
openxlab dataset get --dataset-repo OpenDataLab/KITTI-C
```

Python SDK download:

Install the openxlab package.
```
pip install openxlab
```
Use the following command to download the dataset.

from openxlab.dataset import get
get(dataset_repo='OpenDataLab/KITTI-C', target_path='/path/to/local/folder/')

Alternatively, you can follow the create folder for generating corrupted data.

Your folder structure should end up looking like this:

└── KITTI-C  
    ├── fog
    │    ├── light
    │    │     └── velodyne              
    │    ├── moderate
    │    └── heavy
    ├── wet_ground
    ├── snow
    ├── motion_blur
    ├── beam_missing
    ├── crosstalk
    ├── incomplete_echo
    └── cross_sensor

SemanticKITTI

To install the SemanticKITTI dataset, download the data, annotations, and other files from http://semantic-kitti.org/dataset. Unpack the compressed file(s) into /data/sets/semantickitti and re-organize the data structure. Your folder structure should end up looking like this:

└── semantickitti  
    └── sequences
        ├── velodyne <- contains the .bin files; a .bin file contains the points in a point cloud
        │    └── 00
        │    └── ···
        │    └── 21
        ├── labels   <- contains the .label files; a .label file contains the labels of the points in a point cloud
        │    └── 00
        │    └── ···
        │    └── 10
        ├── calib
        │    └── 00
        │    └── ···
        │    └── 21
        └── semantic-kitti.yaml

SemanticKITTI-C

Download the SemanticKITTI-C dataset from the OpenDataLab platform and unpack it.

Direct download:
- Go to https://opendatalab.com/OpenDataLab/SemanticKITTI-C/tree/main/raw.
- Click the download button on the right to download the dataset.
CLI download:
- Install the openxlab package.
- ```
pip install openxlab
```
- Use the following command to download the dataset.
- ```
openxlab dataset get --dataset-repo OpenDataLab/SemanticKITTI-C
```

Python SDK download:

Install the openxlab package.
```
pip install openxlab
```
Use the following command to download the dataset.

from openxlab.dataset import get
get(dataset_repo='OpenDataLab/SemanticKITTI-C', target_path='/path/to/local/folder/')

Alternatively, you can follow the create folder for generating corrupted data.

Your folder structure should end up looking like this:

└── SemanticKITTI-C  
    ├── fog
    │    ├── light
    │    │     ├── velodyne           
    │    │     └── labels    
    │    ├── moderate
    │    └── heavy
    ├── wet_ground
    ├── snow
    ├── motion_blur
    ├── beam_missing
    ├── crosstalk
    ├── incomplete_echo
    └── cross_sensor

nuScenes

To install the nuScenes dataset, download the data, annotations, and other files from https://www.nuscenes.org/download. Unpack the compressed file(s) into /data/sets/nuscenes and your folder structure should end up looking like this:

└── nuscenes  
    ├── Usual nuscenes folders (i.e. samples, sweep)
    │
    ├── lidarseg
    │   └── v1.0-{mini, test, trainval} <- contains the .bin files; a .bin file 
    │                                      contains the labels of the points in a 
    │                                      point cloud (note that v1.0-test does not 
    │                                      have any .bin files associated with it)
    │
    └── v1.0-{mini, test, trainval}
    |   ├── Usual files (e.g. attribute.json, calibrated_sensor.json etc.)
    |   ├── lidarseg.json  <- contains the mapping of each .bin file to the token   
    |   └── category.json  <- contains the categories of the labels (note that the 
    |                         category.json from nuScenes v1.0 is overwritten)
    |
    └──  nuscenes_infos_val.pkl

Notably, we follow the data pre-processing in Cylinder3D to prepare the nuscenes_infos_val.pkl file for the nuScenes validation set. You can find the nuscenes_infos_val.pkl file from this link.

nuScenes-C

Download the nuScenes-C dataset from the OpenDataLab platform and unpack it.

Direct download:
- Go to https://opendatalab.com/OpenDataLab/nuScenes-C/tree/main/raw/pointcloud.
- Click the download button on the right to download the dataset.
CLI download:
- Install the openxlab package.
- ```
pip install openxlab
```
- Use the following command to download the dataset.
- ```
openxlab dataset get --dataset-repo OpenDataLab/nuScenes-C
```

Python SDK download:

Install the openxlab package.
```
pip install openxlab
```
Use the following command to download the dataset.

from openxlab.dataset import get
get(dataset_repo='OpenDataLab/nuScenes-C', target_path='/path/to/local/folder/')

Alternatively, you can follow the create folder for generating corrupted data. You will need to download the precomputed snowflake patterns in Link and put it into ./Robo3D/create/nuscenes_c/snow folder.

Your folder structure should end up looking like this:

└── nuScenes-C  
    ├── fog
    │    ├── light
    │    │     ├── samples/LIDAR_TOP        
    │    │     └── lidarseg/v1.0-trainval   
    │    ├── moderate
    │    └── heavy
    ├── wet_ground
    ├── snow
    ├── motion_blur
    ├── beam_missing
    ├── crosstalk
    ├── incomplete_echo
    └── cross_sensor

WaymoOpen

Coming soon.

WaymoOpen-C

Coming soon.

References

Please note that you should cite the corresponding paper(s) once you use these datasets.

KITTI

@inproceedings{geiger2012kitti,
    author = {A. Geiger and P. Lenz and R. Urtasun},
    title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages = {3354--3361},
    year = {2012}
}

SemanticKITTI

@inproceedings{behley2019semantickitti,
    author = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall},
    title = {SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
    pages = {9297--9307},
    year = {2019}
}

nuScenes

@article{fong2022panopticnuscenes,
    author = {W. K. Fong and R. Mohan and J. V. Hurtado and L. Zhou and H. Caesar and O. Beijbom and A. Valada},
    title = {Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking},
    journal = {IEEE Robotics and Automation Letters},
    volume = {7},
    number = {2},
    pages = {3795--3802},
    year = {2022}
}

@inproceedings{caesar2020nuscenes,
    author = {H. Caesar and V. Bankiti and A. H. Lang and S. Vora and V. E. Liong and Q. Xu and A. Krishnan and Y. Pan and G. Baldan and O. Beijbom},
    title = {nuScenes: A Multimodal Dataset for Autonomous Driving},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages = {11621--11631},
    year = {2020}
}

Waymo Open

@inproceedings{sun2020waymoopen,
    author = {P. Sun and H. Kretzschmar and X. Dotiwalla and A. Chouard and V. Patnaik and P. Tsui and J. Guo and Y. Zhou and Y. Chai and B. Caine and V. Vasudevan and W. Han and J. Ngiam and H. Zhao and A. Timofeev and S. Ettinger and M. Krivokon and A. Gao and A. Joshi and Y. Zhang and J. Shlens and Z. Chen and D. Anguelov},
    title = {Scalability in Perception for Autonomous Driving: Waymo Open Dataset},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages = {2446--2454},
    year = {2020}
}

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DATA_PREPARE.md

DATA_PREPARE.md

Data Preparation

Overall Structure

KITTI

KITTI-C

SemanticKITTI

SemanticKITTI-C

nuScenes

nuScenes-C

WaymoOpen

WaymoOpen-C

References

KITTI

SemanticKITTI

nuScenes

Waymo Open

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DATA_PREPARE.md

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DATA_PREPARE.md

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Data Preparation

Overall Structure

KITTI

KITTI-C

SemanticKITTI

SemanticKITTI-C

nuScenes

nuScenes-C

WaymoOpen

WaymoOpen-C

References

KITTI

SemanticKITTI

nuScenes

Waymo Open