Depth From Focus Competition on the DDFF 12-Scene Dataset
Current
TESTEnd
Competition EndsWelcome to Depth from Focus competition
This is a competition for depth from focus methods. Main problem is defined as predicting an accurate depth map from a focal strack, where the focus gradually changes from close to far away distances. Evaluation of the methods is performed on the DDFF 12-Scene dataset.
Please cite following paper if you participate in the competition:
@inproceedings{hazirbas18ddff,
author = {C. Hazirbas and S. Soyer and M. Staab and L. Leal-Taixé and D. Cremers},
title = {Deep Depth From Focus},
month = {December},
year = {2018},
booktitle = {ACCV},
eprint = {1704.01085},
url = {https://hazirbas.com/projects/ddff/}
}
Evaluation Metrics
Results are reported for different error metrics, computed between the predicted and ground-truth dispariy maps. Errors are computed for the interval of [0.28, 0.02] pixel, equivalent to [0.5, 7] meters. MSE and RMS are also reported for actual depth errors.
Disparity maps must be saved as float arrays under each test folder using numpy.save() function.
Example: cafeteria/DISP_0001.npy where DISP_0001.npy is disparity map.
"runtime.txt" should be included where each line provides the runtime for each image in seconds.
Example: cafeteria/DISP_0001 0.6543
Please see the following paper for details.
@inproceedings{hazirbas17ddff,
title = {Deep Depth From Focus},
author = {C. Hazirbas and L. Leal-Taixé and D. Cremers},
booktitle = {Arxiv preprint arXiv:1704.01085},
month = {April},
year = {2017},
}
Terms and Conditions
All data in the DDFF 12-Scene benchmark is licensed under a Creative Commons 4.0 Attribution License (CC BY 4.0).
TEST
Start: Dec. 14, 2017, midnight
Competition Ends
Never
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