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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



4Seasons Dataset

4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving

We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving. Among others, it enables research on visual odometry, global place recognition, and map-based re-localization tracking. The data was collected in different scenarios and under a wide variety of weather conditions and illuminations, including day and night. This resulted in more than 350 km of recordings in nine different environments ranging from multi-level parking garage over urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up-to centimeter accuracy obtained from the fusion of direct stereo visual-inertial odometry with RTK-GNSS.

For additional information, please visit the official 4Seasons-dataset webpage.

Dataset Tools

To make the work with our dataset as convenient as possible we are providing a collection of python tools in a GitHub repository.


If you would like to download the 4Seasons dataset, please fill out this form.


All datasets and benchmarks on this page are copyright by Artisense and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. Per GDPR requirements, to download and use the data you need to register and specify the intended purpose of using the dataset.

Exclusive commercial rights for the 4Seasons dataset are held by Artisense. For commercial licensing of the dataset please contact licensing@artisense.ai.


Test vehicle and sensor system (Artisense VINS) used for dataset recording. Our sensor setup consists of two monochrome cameras in a stereo setup and an IMU. The system is coupled with an high-end RTK-GNSS receiver for global positioning. For more details, please refer to the paper.


If you find our dataset useful, please cite the following paper:

  author = {P. Wenzel and R. Wang and N. Yang and Q. Cheng and Q. Khan and 
  L. von Stumberg and N. Zeller and D. Cremers},
  title = {{4Seasons}: A Cross-Season Dataset for Multi-Weather {SLAM} in Autonomous Driving},
  booktitle = {Proceedings of the German Conference on Pattern Recognition ({GCPR})},
  year = {2020}

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Conference and Workshop Papers
[]4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving (P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. von Stumberg, N. Zeller and D. Cremers), In Proceedings of the German Conference on Pattern Recognition (GCPR), 2020. ([project page][arXiv][video]) [bibtex] [pdf]
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This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or your personal belongings in this dataset and feel unwell about it, please contact us 4seasons-dataset@artisense.ai, and we will immediately remove the respective data from our server.

We have been organizing workshops at international research conferences (i.e., ECCV, ICCV) to discuss the topic of map-based localization in autonomous driving with experts from industry and academia. In conjunction with the workshop, we are regularly organizing challenges based on the 4Seasons dataset.

Recent workshops:

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CVPR 2023

We have six papers accepted to CVPR 2023.


NeurIPS 2022

We have two papers accepted to NeurIPS 2022.


WACV 2023

We have two papers accepted at WACV 2023.


Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.


MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.