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DATA_PREPARE.md

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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.

nuScenes-C

This dataset is pending release for a careful check of potential IP issues. If you would like to test the robustness of your model in the current stage, please seek a solution from the following two options:

  • Get in touch with us; include the inference code and the model checkpoint and we will evaluate it for you.
  • Generate the corruption sets by yourself and evaluate the model performance accordingly. Kindly refer to more details in this page.

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}
}