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Datasets:
TedLentsch
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nuScenes-UNION-labels

Tasks:
Object Detection
Languages:
English
ArXiv:
Tags:
mmdetection3d
nuscenes
pseudo-labels
robotics
union
unsupervised
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metadata 
pretty_name: nuScenes-UNION-labels
license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
tags:
  - mmdetection3d
  - nuscenes
  - pseudo-labels
  - robotics
  - union
  - unsupervised
language:
  - en
arxiv: 2405.15688
papers:
  - title: >-
      UNION: Unsupervised 3D Object Detection using Appearance-based
      Pseudo-Classes
    url: https://arxiv.org/abs/2405.15688
    conference: NeurIPS 2024
citations:
  - type: arxiv
    url: https://arxiv.org/abs/2405.15688
  - type: google_scholar
    url: >-
      https://scholar.google.com/citations?view_op=view_citation&hl=en&user=54NWkMoAAAAJ&citation_for_view=54NWkMoAAAAJ:roLk4NBRz8UC
  - type: bibtex
    content: |
      @inproceedings{lentsch2024union,
        title={{UNION}: Unsupervised {3D} Object Detection using Object Appearance-based Pseudo-Classes},
        author={Lentsch, Ted and Caesar, Holger and Gavrila, Dariu M},
        booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
        year={2024}
      }

UNION: Unsupervised 3D Object Detection using Appearance-based Pseudo-Classes [NeurIPS 2024]

[arXiv] [GitHub] [BibTeX]

Hugging Face repository for prebuilt .pkl annotation files created by UNION on the nuScenes dataset.

This repo is annotations-only (no sensor data) so you can reproduce UNION results quickly without regenerating labels.

Pipeline Overview

Contents

This repo contains three top-level folders, namely standard-labels, class-agnostic-labels, and multi-class-003-labels. The folder standard-labels contains the MMDetection3D infos from the ground truth annotations for the standard dataset split and classes. The folders class-agnostic-labels and multi-class-003-labels contain the ground truth labels and pseudo-labels for class agnostic and multi-class (vehicle, pedestrian, cyclist) 3D object detection, respectively.

Citation Information 

@inproceedings{caesar2020nuscenes,
  title={{nuScenes}: A multimodal dataset for autonomous driving},
  author={Caesar, Holger and Bankiti, Varun and Lang, Alex H and Vora, Sourabh and Liong, Venice Erin and Xu, Qiang and Krishnan, Anush and Pan, Yu and Baldan, Giancarlo and Beijbom, Oscar},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={11621--11631},
  year={2020}
}

@misc{mmdet3d2020,
  title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
  author={MMDetection3D Contributors},
  howpublished={\url{https://github.com/open-mmlab/mmdetection3d}},
  year={2020}
}

@inproceedings{lentsch2024union,
  title={{UNION}: Unsupervised {3D} Object Detection using Object Appearance-based Pseudo-Classes},
  author={Lentsch, Ted and Caesar, Holger and Gavrila, Dariu M},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  pages={22028--22046},
  volume={37},
  year={2024}
}

Questions & issues

License

This repository is distributed under CC BY-NC-SA 4.0 to comply with the nuScenes license. Non-commercial use only, and derivatives must be shared under the same license.

  • You must also comply with the original nuScenes license when using these annotations.
  • If you use these files, please cite nuScenes, MMDetection3D, and UNION.

Note: UNION code base itself is released under Apache-2.0 license, which allows for commercial use!

License: CC BY-NC-SA 4.0