Synthetic outdoor driving dataset with smooth transitions between domains (day-night, sunny-raining etc.)

Released in: SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation



Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video driving datasets, however, fall short of capturing the mutable nature of the real world. With SHIFT, the authors introduce the largest multi-task synthetic dataset for autonomous driving. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows investigating the degradation of a perception system performance at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assess model robustness and generality.


Images in dataset


Year Released

Key Links & Stats

@InProceedings{shift2022, author = {Sun, Tao and Segu, Mattia and Postels, Janis and Wang, Yuxuan and Van Gool, Luc and Schiele, Bernt and Tombari, Federico and Yu, Fisher}, title = {{SHIFT:} A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {21371-21382} }



  1. Video
  2. RGB-D
  3. LiDAR


  1. A/V

ML Task

  1. Object Detection
  2. Semantic Segmentation
  3. Instance Segmentation
  4. Domain Adaptation
  5. Autonomous Vehicles

Related organizations

ETH Zurich

MPI Informatics

Technical University of Munich