Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving

Alexey Nekrasov, Malcolm Burdorf, Stewart Worrall, Bastian Leibe Julie Stephany Berrio Perez

RWTH Aachen University, The University of Sydney

[Project Webpage]

News

• 2024-03-25: Data and Evaluation Code Release
• 2024-02-26: STU Accepted at CVPR 2025

Data

• STU dataset is available at STU.
• PANOPTIC-CUDAL dataset is available at Panoptic-CUDAL.

To verify that downloaded files are correct, you can verify the SHA256 hash of the files.

sha256sum -c file_sha256sum.chk

Overall the data follows the SemanticKITTI format.

|── 125/
|   ├── poses.txt
|   ├── calib.txt
|   ├── labels/
|   │     ├ 000000.label
|   │     └ 000001.label
|   .
|   |
|   └── velodyne/
|         ├ 000000.bin
|         └ 000001.bin
.
.
└── 134/

Predictions are simple .txt files with confidence per point.

Evaluation

Simple evaluation for point-level anomaly segmentation:

python compute_point_level_ood.py --data-dir stu_dataset/val --pred-dir ./prediction

Simple evaluation for point-level anomaly segmentation:

python compute_object_level_ood.py --data-dir stu_dataset/val --instance-dir ./instance_prediction

TODO

• Release images
• Release training code and checkpoints
• Release code for points projection to images
• Release the data
• Release evaluation code

BibTeX

@inproceedings{nekrasov2025stu,
  title = {{Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving}},
  author = {Nekrasov, Alexey and Burdorf, Malcolm and Worrall, Stewart and Leibe, Bastian and Julie Stephany Berrio Perez},
  booktitle = {{"Conference on Computer Vision and Pattern Recognition (CVPR)"}},
  year = {2025}
}

LICENSE

MIT!