Followed from https://github.com/JiaRenChang/PSMNet
-
Python 3.5
-
PyTorch 0.4.0+
-
torchvision 0.2.0
-
KITTI 2012 / KITTI 2015
-
Scene Flow
KITTI: The KITTI dataset is relatively small and has real-world pictures with sparse ground truth disparity maps.
Scene Flow: The Scene Flow dataset is relatively large. It is a synthetic dataset with dense ground truth disparity maps.
Training Strategy: Pretrain the network using Scene Flow dataset, then finetune the network on the KITTI dataset
python main.py --maxdisp 192 \
--datapath /SceneFlow_dataset_path \
--epochs 10 \
--savemodel ./trained/
python finetune.py --maxdisp 192 \
--datatype 2015 \
--datapath /KITTI2_train_set_path \
--epochs 600 \
--loadmodel ./trained/checkpoint_10.tar \
--savemodel ./trained/trained2015
python submission.py --loadmodel ./trained/trained2012/finetune_600.tar \
--KITTI 2012
--datapath /KITTI_test_set_path
The disparity maps for the KITTI testset are calculated by submission.py, those disparity maps are submitted to the KITTI website to calculated the final accuracy result.
@article{luan2018gabor,
title={Gabor convolutional networks},
author={Luan, Shangzhen and Chen, Chen and Zhang, Baochang and Han, Jungong and Liu, Jianzhuang},
journal={IEEE Transactions on Image Processing},
volume={27},
number={9},
pages={4357--4366},
year={2018},
publisher={IEEE}
}
@inproceedings{liu2018stereo,
title={Stereo Matching Using Gabor Convolutional Neural Network},
author={Liu, Zhendong and Hu, Qinglei and Liu, Jiachen and Zhang, Baochang},
booktitle={2018 11th International Workshop on Human Friendly Robotics (HFR)},
pages={48--53},
year={2018},
organization={IEEE}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent