lfattnet's Introduction

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

(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

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