Simom0/OoD-Datasets

OoD Datasets

OoD Datasets from "Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation" (paper).

Download the datasets and extract them:

• SemanticKITTI-OoD: git clone https://huggingface.co/datasets/Simom0/OoD-Datasets/resolve/main/SemanticKITTI-OoD.zip

• SemanticPOSS-OoD: git clone https://huggingface.co/datasets/Simom0/OoD-Datasets/resolve/main/SemanticPOSS-OoD.zip

• nuScenes-OoD: git clone https://huggingface.co/datasets/Simom0/OoD-Datasets/resolve/main/nuScenes-OoD.zip

Each zip file contains the two splits single and multi. E.g. for SemanticKITTI-OoD, there are two folders insied, kitti-ood for the single split and kitti-ood-multi for the multi split.

For further details, please see the official repo LIDO and the DATA.md preparation file.

License

The proposed OoD dataset are based on the SemanticKITTI, SemanticPOSS and nuScenes benchmarks and therefore we distribute the data under Creative Commons Attribution-NonCommercial-ShareAlike license. You are free to share and adapt the data, but have to give appropriate credit and may not use the work for commercial purposes. Please refer to the original license of each dataset.

OoD Dataset	Original Dataset	Original License	Reference
SemanticKITTI-OoD	SemanticKITTI	CC BY-NC-SA 4.0	link
SemanticPOSS-OoD	SemanticPOSS	CC BY-NC-SA 3.0	link
nuScenes-OoD	nuScenes	CC BY-NC-SA 4.0	link

Specifically, you should cite our work (PDF):

@inproceedings{mosco2026learning,
    title={Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation},
    author={Mosco, Simone and Fusaro, Daniel and Pretto, Alberto},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2026}
}

But also the original datasets SemanticKITTI, SemanticPOSS and nuScenes:

@inproceedings{behley2019iccv,
  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 = {Proc. of the IEEE/CVF International Conf.~on Computer Vision (ICCV)},
  year = {2019}
}

@inproceedings{pan2020semanticposs,
  title={Semanticposs: A point cloud dataset with large quantity of dynamic instances},
  author={Pan, Yancheng and Gao, Biao and Mei, Jilin and Geng, Sibo and Li, Chengkun and Zhao, Huijing},
  booktitle={2020 IEEE intelligent vehicles symposium (IV)},
  pages={687--693},
  year={2020},
  organization={IEEE}
}

@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={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={11621--11631},
  year={2020}
}