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README.md

DeOccNet

This is the repository of our WACV 2020 paper "DeOccNet: Learning to See Through Foreground Occlusions in Light Fields". arXiv

PyTorch implementation of DeOccNet

The project of DeOccNet can be downloaded here.

Requirements

Environment:

  • 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);

Hardware configuration:

  • 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

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 Yingqian Wang (wangyingqian16@nudt.edu.cn) for any question about this work.

About

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

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