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]
- 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
Ubuntu 16.04
Python 3.5.2
Tensorflow-gpu 1.10
CUDA 9.0.176
Cudnn 7.1.4
- 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'.
- 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'
- To use your own model, you can modify the import model at line 78 like below:
@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