This is a PyTorch implementation for our WACV 2018 paper "Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction".
Note: The pretrained models are trained on the split1 of following larger dataset.
- The code is developed with CUDA 9.0, Python >= 3.6, PyTorch >= 1.0
-
Download the raw data at
ftp://data.cresis.ku.edu/data/rds/2014_Greenland_P3/CSARP_music3D/- If you don't want to preprocess the raw data by yourself, please use
create_slices.mto generate radar images andconvert_mat_to_npy.pyto convert them from MATLAB to NumPy files.
- If you don't want to preprocess the raw data by yourself, please use
-
If you want to use our dataloaders, please make sure to put the files as the following structure:
$YOUR_PATH_TO_CRESIS_DATASET ├── slices_mat_64x64/ | ├── 20140325_05/ | | ├── 001/ | | | ├── 00001.mat | | | ├── ... | | ├── ... │ ├── ... | ├── slices_npy_64x64/ | ├── 20140325_05/ | | ├── 001/ | | | ├── 00001.npy | | | ├── ... | | ├── ... | ├── ... -
Create softlinks of datasets:
cd ice-WACV2018 ln -s $YOUR_PATH_TO_CRESIS_DATASET data/CReSIS ln -s data/target data/CReSIS/target
- Download the pretrained models at
model_zoo.
- C3D
cd ice-WACV2018
# Default Hyperparameters
python tools/c3d/train.py
# OR
python tools/c3d/train.py --gpu $CUDA_VISIBLE_DEVICES --batch_size $BS --lr $LR
- Extract C3D Features
cd ice-WACV2018
# Default Hyperparameters
python tools/c3d/extract_features.py
# OR
python tools/c3d/extract_features.py --gpu $CUDA_VISIBLE_DEVICES --batch_size $BS --checkpoint $C3D_CHECKPOINT
- RNN
cd ice-WACV2018
# Default Hyperparameters
python tools/rnn/train.py
# OR
python tools/rnn/train.py --gpu $CUDA_VISIBLE_DEVICES --batch_size $BS --lr $LR
cd ice-WACV2018
# Default Hyperparameters
python demo/e2e_eval.py
# OR
python demo/e2e_eval.py --gpu $CUDA_VISIBLE_DEVICES --batch_size $BS --c3d_pth $C3D_CHECKPOINT --rnn_pth $RNN_CHECKPOINT
If you are using the data/code/model provided here in a publication, please cite our papers:
@inproceedings{icesurface2018wacv,
title = {Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction},
author = {Mingze Xu and Chenyou Fan and John D. Paden and Geoffrey C. Fox and David J. Crandall},
booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
year = {2018}
}
@inproceedings{icesurface2017icip,
title = {Automatic estimation of ice bottom surfaces from radar imagery},
author = {Mingze Xu and David J. Crandall and Geoffrey C. Fox and John D. Paden},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = {2017}
}
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