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Data Preparation

Overall Structure

└── data 
    └── sets
        │── nuscenes
        └── nuscenes-c

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)

Please follow the official instructions of each model repo to process the nuScenes dataset. It's recommend to use the absolute dataset path when generate the .pkl annotation file.

To generate domain-specific annotation, please use the following command to generate the domain annotation files.

cd ./uda
bash tools/create_data.sh

The domain config includes city2city, day2night, and dry2rain.

nuScenes-C

The dataset is now available at OpenDataLab, you can download the dataset here. The dataset used in this work only contains the raw/image/nuScenes-C.tar.gz file. If you want to use the raw/pointcloud part, please refer to Robo3D.

Unpack the compressed file(s) into /data/sets/nuscenes-c and your folder structure should end up looking like this:

└── nuscenes-c  
    ├── Camera
    │   ├── easy <- contains folders the same as
    │   │           in `nuscenes/samples` folder
    │   ├── mid
    │   └── hard
    │
    ├── Frame
    │   ├── easy 
    │   ├── mid
    │   └── hard
    │
    └── ...

References

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

@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}
}
@article{xie2023robobev,
    title = {RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions},
    author = {Xie, Shaoyuan and Kong, Lingdong and Zhang, Wenwei and Ren, Jiawei and Pan, Liang and Chen, Kai and Liu, Ziwei},
    journal = {arXiv preprint arXiv:2304.06719}, 
    year = {2023}
}