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 Chuang^1
1National Taiwan University, ²MediaTek

AAAI Conference on Artificial Intelligence (AAAI), Feb 2020

• (Paper Link)
• Contact: [email protected], [email protected]

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