"DeOccNet: Learning to See Through Foreground Occlusions in Light Fields". WACV 2020

The codes of DeOccNet can be downloaded via this link.

Requirements：

• python 3.7, cuda 9.2, cudnn 7.0, pytorch 1.3.0, torchvision 0.4.1;

• numpy 1.16.4+mkl, opencv-python 4.1.0.25 (only used for test);

• Matlab 2018a (for training and test data generation);

• Nvidia GPU (trained on RTX2080Ti, 11GB Memory);

• More than 500GB disk space to store training data (Here, an SSD is preferred);

• More than 32GB RAM is preferred since we do not perform cropping or resizing during test;

Test:

• Prepare test LFs in folder Dataset;
• Run GenerateDataForTest.m to generate test data;
• Execute test25.py or test75.py to implement DeOccNet for test;

Train:

• Prepare training LFs in folder Dataset using the Mask Embedding approach;
• Run GenerateDataForTraining.m to generate training data (over 300 GB);
• Execute train.py to train DeOccNet on the generated data;

The Mask Embedding Approach:

• Codes can be downloaded here.
• Note: LFs in folders LF_original_5 and LF_original_15 can be downloaded via Baidu Drive.

Datasets:

• Synthetic datasets rendered using 3dsMax. download
• Real-world datasets captured using cameras on a gantry. download

Citiations:

@InProceedings{DeOccNet,
     author = {Wang, Yingqian and Wu, Tianhao and Yang, Jungang and Wang, Longguang and An, Wei and Guo, Yulan},
      title = {De{O}cc{N}et: Learning to See Through Foreground Occlusions in Light Fields},
  booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
      month = {Mar},
       year = {2020}
}

Contact:

Please contact [email protected] for any question about this work.