DSEC-Flow: Optical Flow Benchmark

The task is to estimate dense optical flow from event cameras. Global shutter cameras can be used as well but their use must be specified in the submission.

Details

Metrics

The main metric used for the ranking is the end-point-error (EPE) of the optical flow on the test set of DSEC-Flow.

We also provide the following error metrics:

A detailed explanation of EPE and AE can be found in “A Database and Evaluation Methodology for Optical Flow“.

Submission Format

The submission will be evaluated on the predicted forward optical flow 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

Questions

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

Citation

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

@InProceedings{Gehrig3dv2021,
  author = {Mathias Gehrig and Mario Millh\"ausler and Daniel Gehrig and Davide Scaramuzza},
  title = {E-RAFT: Dense Optical Flow from Event Cameras},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2021}
}

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Results

All sequence averages
Individual sequences