Disparity Benchmark

The task is to estimate dense disparity from stereo event cameras and stereo global shutter cameras in the event camera frame.



The main metric used for the ranking is the mean absolute error (MAE) of the disparity on the test set of DSEC.

We also provide the following error metrics:

Submission Format

The submission will be evaluated on the predicted disparity in the left event camera on a subset of image timestamps on the test set. Visit the submission format page for more details.

Report (mandatory)

The submission must eventually be accompanied by a

In either case, specify the venue (competition, workshop, conference, or journal and year) or write “under review” in the “Additional information” field of the submission.

Note that you can still add a report/paper after your submission.

We will regularly delete submissions older than 6 months without a report or paper or the specifics to identify the venue. If your work has been under review for more than 6 months you must resubmit your work.

Note on Double-Blind Review

If your submitted work is under review in a double-blind process. Consider adding “anonymous” to the author and affiliation field for the submission (as well as “under review” in the “additional information” field). When the work is accepted, you can delete the submission and resubmit with the full information mentioned above.

Submission Policy


Contact mgehrig@ifi.uzh.ch for inquiries regarding the benchmark. Issues and questions regarding the dataset should be discussed on GitHub instead.


Cite the following work when using this benchmark or DSEC in general:

  author  = {Mathias Gehrig and Willem Aarents and Daniel Gehrig and Davide Scaramuzza},
  title   = {DSEC: A Stereo Event Camera Dataset for Driving Scenarios},
  journal = {IEEE Robotics and Automation Letters},
  year    = {2021},
  doi     = {10.1109/LRA.2021.3068942}


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All sequence averages
Individual sequences