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Attention-based View Selection Networks for Light-field Disparity Estimation

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LFattNet: Attention-based View Selection Networks for Light-field Disparity Estimation

Attention-based View Selection Networks for Light-field Disparity Estimation

Yu-Ju Tsai,1 Yu-Lun Liu,1,2 Ming Ouhyoung,1 Yung-Yu Chuang1
1National Taiwan University, 2MediaTek

AAAI Conference on Artificial Intelligence (AAAI), Feb 2020

Network Architecture

Network Architecture

SOTA on 4D Light Field Benchmark

  • We achieve TOP rank performance for most of the error matrices on the benchmark.

  • For more detail comparison, please use the link below.
  • Benchmark link

Environment

Ubuntu            16.04
Python            3.5.2
Tensorflow-gpu    1.10
CUDA              9.0.176
Cudnn             7.1.4

Train LFattNet

  • Download HCI Light field dataset from http://hci-lightfield.iwr.uni-heidelberg.de/.
  • Unzip the LF dataset and move 'additional/, training/, test/, stratified/ ' into the 'hci_dataset/'.
  • Check the code in 'LFattNet_func/func_model_81.py' and use the code at line 247.
  • Run python LFattNet_train.py
    • Checkpoint files will be saved in 'LFattNet_checkpoints/LFattNet_ckp/iterXXXX_valmseXXXX_bpXXX.hdf5'.
    • Training process will be saved in
      • 'LFattNet_output/LFattNet/train_iterXXXXX.jpg'
      • 'LFattNet_output/LFattNet/val_iterXXXXX.jpg'.

Evaluate LFattNet

  • Check the code in 'LFattNet_func/func_model_81.py' and use the code at line 250.
  • Run python LFattNet_evaluation.py
    • To use your own model, you can modify the import model at line 78 like below:
      • path_weight='./pretrain_model_9x9.hdf5'

Citation

@inproceedings{Tsai:2020:ABV,
        author = {Tsai, Yu-Ju and Liu, Yu-Lun and Ouhyoung, Ming and Chuang, Yung-Yu},
        title = {Attention-based View Selection Networks for Light-field Disparity Estimation},
        booktitle = {Proceedings of the 34th Conference on Artificial Intelligence (AAAI)},
        year = {2020}
}

Last modified data: 2020/09/14.

The code is modified and heavily borrowed from EPINET: https://github.com/chshin10/epinet

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