Awesome
Fast Moving Object (FMO) Deblurring Benchmark
Simple Python library to evaluate your FMO deblurring methods.
Datasets
All three datasets (TbD, TbD-3D, Falling Objects) can be downloaded by running (after modifying the data storage folder path):
bash download_datasets.sh
Usage
Implement a function that takes as input image I [w, h, 3]
, background B [w, h, 3]
, bounding box of approximation FMO location, the required number of generated sub-frames n
(temporal super-resolution), and an approximate object size. Your method should output temporal super-resolution mini-video of size [w, h, 3, n]
. Optionally, if you want to evaluate trajectory accuracy, output the sub-frame object trajectory of size [2, n]
or None
.
An example of a dummy algorithm that always outputs the input image and does not evaluate the trajectory accuracy:
def my_deblur(I,B,bbox,nsplits,radius):
return np.repeat(I[:,:,:,None], nsplits, 3), None
Baselines
We provide several baseline and state-of-the-art methods.
Dummy baselines
Two baselines, one that always outputs the input image, and another that output the background image. Example is shown in example_dummy.py
.
Deblatting
To evaluate this method, please check out the deblatting sub-module. We provide three versions of deblatting: classical deblatting with single appearance (TbD), deblatting with chanring appearance (TbD-3D), and deblatting with trajectory oracle (TbD-O). Examples are shown in example_deblatting.py
.
DeFMO - current state-of-the-art
The easiest way to evaluate DeFMO is using Kornia (kornia.feature.DeFMO
). Example is shown in example_defmo.py
.
To evaluate this method using the original source coude, please download DeFMO. Example is shown in example_defmo_source.py
.
Scores
TbD-3D-Oracle has access to the ground-truth trajectory. Therefore, it's not a competitive baseline and is provided just for the reference.
Falling Objects dataset
Arbitrary shaped and textured objects.
Score | Bg | Im | Jin et al. | DeblurGAN-v2 | TbD | TbD-3D | DeFMO | (TbD-3D-Oracle) |
---|---|---|---|---|---|---|---|---|
TIoU | 0 | 0 | 0 | 0 | 0.539 | 0.539 | 0.684 | 1.000 |
PSNR | 19.71 | 23.76 | 23.54 | 23.36 | 20.53 | 23.42 | 26.83 | 23.38 |
SSIM | 0.456 | 0.594 | 0.575 | 0.588 | 0.591 | 0.671 | 0.753 | 0.692 |
TbD-3D dataset
Mostly spherical but significantly textured objects moving in 3D.
Score | Bg | Im | Jin et al. | DeblurGAN-v2 | TbD | TbD-3D | DeFMO | (TbD-3D-Oracle) |
---|---|---|---|---|---|---|---|---|
TIoU | 0 | 0 | 0 | 0 | 0.598 | 0.598 | 0.879 | 1.000 |
PSNR | 19.81 | 24.80 | 24.52 | 23.58 | 18.84 | 23.13 | 26.23 | 24.84 |
SSIM | 0.426 | 0.640 | 0.590 | 0.603 | 0.504 | 0.651 | 0.699 | 0.705 |
TbD dataset
Mostly spherical and uniformly colored objects moving in a plane parallel to the camera plane.
Score | Bg | Im | Jin et al. | DeblurGAN-v2 | TbD | TbD-3D | DeFMO | (TbD-3D-Oracle) |
---|---|---|---|---|---|---|---|---|
TIoU | 0 | 0 | 0 | 0 | 0.542 | 0.542 | 0.550 | 1.000 |
PSNR | 21.48 | 25.06 | 24.90 | 24.27 | 23.22 | 25.21 | 25.57 | 26.36 |
SSIM | 0.466 | 0.568 | 0.530 | 0.537 | 0.605 | 0.674 | 0.602 | 0.712 |
Reference
If you use this repository, please cite the following publication:
@inproceedings{defmo,
author = {Denys Rozumnyi and Martin R. Oswald and Vittorio Ferrari and Jiri Matas and Marc Pollefeys},
title = {DeFMO: Deblurring and Shape Recovery of Fast Moving Objects},
booktitle = {CVPR},
address = {Nashville, Tennessee, USA},
month = jun,
year = {2021}
}
The baseline TbD method:
@inproceedings{Kotera-et-al-ICCVW-2019,
author = {Jan Kotera and Denys Rozumnyi and Filip Sroubek and Jiri Matas},
title = {Intra-frame Object Tracking by Deblatting},
booktitle = {Internatioal Conference on Computer Vision Workshop (ICCVW),
Visual Object Tracking Challenge Workshop, 2019},
address = {Seoul, South Korea},
month = oct,
year = {2019}
}
The baseline TbD-3D or TbD-O method:
@inproceedings{Rozumnyi-et-al-CVPR-2020,
author = {Denys Rozumnyi and Jan Kotera and Filip Sroubek and Jiri Matas},
title = {Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects},
booktitle = {CVPR},
address = {Seattle, Washington, USA},
month = jun,
year = {2020}
}
Some ideas are taken from:
@inproceedings{Rozumnyi-et-al-CVPR-2017,
author = {Denys Rozumnyi and Jan Kotera and Filip Sroubek and Lukas Novotny and Jiri Matas},
title = {The World of Fast Moving Objects},
booktitle = {CVPR},
address = {Honolulu, Hawaii, USA},
month = jul,
year = {2017}
}