The ice-wacv2018 from vlbpdasilva

ice-wacv2018's Introduction

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Created by Mingze Xu at Indiana University, Bloomington, IN

Note: The released code and pretrained models are reimplemented on the following larger dataset.

Environment

The code is developed with CUDA 8.0, Python 2.7, PyTorch >= 0.4

Data Preparation

Download the raw data at ftp://data.cresis.ku.edu/data/rds/2014_Greenland_P3/CSARP_music3D/

Download the human-labled annotations at ./data/target.tar.gz

If you don't want to preprocess the data yourself, please use create_slices.m to generate radar images and convert_mat_to_npy.py to convert them from MATLAB to NumPy files.

And make sure to put the files as the following structure:

data_root
├── slices_mat_64x64
|   ├── 20140325_05
│   ├── 20140325_06
|   ├── 20140325_07
│   ├── ...
|
├── slices_npy_64x64
|   ├── 20140325_05
│   ├── 20140325_06
|   ├── 20140325_07
|   ├── ...
|
└── target
    ├── Data_20140325_05_001.txt
    ├── Data_20140325_05_002.txt
    ├── Data_20140325_06_001.txt
    ├── ...

Pretrained Models

Download the pretrained model at ./pretrained_models

Demo

To run the demo:

python demo.py --data_root {path/to/data_root} --c3d_pth {path/to/the/c3d.pth} --rnn_pth {path/to/the/c3d.pth}

Citations

If you are using the data/code/model provided here in a publication, please cite our papers:

@inproceedings{ice2018wacv, 
    title = {Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction},
    author = {Mingze Xu and Chenyou Fan and John Paden and Geoffrey Fox and David 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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