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CycleST (SPL2020)

paper video
This is an implementation of the paper "Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency" in TensorFlow 2. If you find this code useful in your research, please consider citing [1, 2] and

@article{gao2020self,
  title={Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency},
  author={Gao, Yuan and Bregovic, Robert and Gotchev, Atanas},
  journal={IEEE Signal Processing Letters},
  volume={27},
  pages={1425--1429},
  year={2020}
}

This code has been tested on an Ubuntu 18.04 system using TensorFlow 2.6.1 and an NVIDIA GeForce RTX 2080 Ti GPU. If you have any question, please contact the first author at yuan.gao@tuni.fi.

Getting started

1. Python requirements

This demo relies on TensorFlow 2, Python Imaging Library (PIL) and SciPy:

$ git clone --recurse-submodules https://github.com/ygaostu/CycleST.git
$ cd CycleST
$ docker build -t tf2:1.0 .

2. Prepare datasets

A demo dataset ./demo/tower_r_5 is prepared here. Speicifially, this demo dataset is the 5-th horizontal-parallax light field of the 4D light field Tower from the 4D Light Field Benchmark [3]:

As can be seen from the above figure, the demo 3D light field has a minimum disparity of -3.6 pixels and a maximum disparity of 3.5 pixels. In addition, the demo 3D light field tower_r_5 has 9 images (0001-0009.png), of which each has the same size of 512 x 512 pixels.

3. Shearlet system construction

The construction of the elaborately-tailored shearlet system comes from this Github repository. The created mat file st_255_255_5.mat is placed in the ./model/shearlet_systems folder.

4. Horizontal-parallax light field reconstruction

The goal of this demo is to reconstruct the above demo 3D light field tower_r_5 from a Sparsely-Sampled Light Field (SSLF) with only three images: 0001.png, 0005.png and 0009.png. In other words, to generate this SSLF, the interpolation rate should be set to 4. Besides, the generated input SSLF has a minimum disparity of -14.4(=-3.6 x 4) pixels, a maximum disparity of 14(=3.5 x 4) pixels and a disparity range of 28.4(=14.4+14) pixels. Note that the pre-trained model of CycleST is suitable for light field reconstruction on any input SSLF with a disparity range up to 32 pixels. The below cmd can be used to evaluate the 3D light field reconstruction performance of CycleST:

$ docker run --gpus all --env CUDA_VISIBLE_DEVICES=0 -v $PWD:/data -w /data --user $(id -u):$(id -g) -it --rm tf2:1.0 \
python predict.py --path_base=./demo --name_lf=tower_r_5 --angu_res_gt=9 --dmin=-3.6 --dmax=3.5 --interp_rate=4

The reconstructed horizontal-parallax light field is saved in ./demo/tower_r_5_lf_rec. The intermediate results, i.e. reconstruced densely-sampled EPIs, are save in ./demo/tower_r_5_epi_rec.

5. Full-parallax light field reconstruction

In addition to the above 3D light field reconstruction, CycleST can also be applied to full-parallax (4D) light field reconstruction. Similarly, we prepare a demo 4D light field in ./demo/tower_4d [3]. We use the same parameter configuration as the previous step to enhance the angular resolution from 3 x 3 to 9 x 9.

$ docker run --gpus all --env CUDA_VISIBLE_DEVICES=0 -v $PWD:/data -w /data --user $(id -u):$(id -g) -it --rm tf2:1.0 \
python predict.py --path_base=./demo --name_lf=tower_4d --angu_res_gt=9 --dmin=-3.6 --dmax=3.5 --interp_rate=4 --full_parallax

Please refer to Fig. 6 (a) in [1] to understand how to leverage 3D light field reconstruction approaches to perform the full-parallax light fied reconstruction. The reconstructed 4D light field is saved in ./demo/tower_4d_lf_rec.

References

[1] S. Vagharshakyan, R. Bregovic, and A. Gotchev, "Light field reconstruction using shearlet transform," IEEE TPAMI, vol. 40, no. 1, pp. 133–147, 2018.
[2] S. Vagharshakyan, R. Bregovic, and A. Gotchev, "Accelerated shearlet-domain light field reconstruction," IEEE JSTSP, vol. 11, no. 7, pp. 1082–1091, 2017.
[3] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A dataset and evaluation methodology for depth estimation on 4d light fields," ACCV, pp. 19-34, 2016.

Acknowledgments

This work was supported by the project “Modeling and Visualization of Perceivable Light Fields” funded by Academy of Finland under grant No. 325530 and carried out with the support of Centre for Immersive Visual Technologies (CIVIT) research infrastructure, Tampere University, Finland.