Awesome
Sat-MVS: Multi-View Stereo Dense Matching Network for Satellite Images
Official Implementation of ICCV2020: Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching
Requirements
For more details, please refer to environment.yaml. And You can simply import this environment from the yaml file via conda:
conda env create -f environment.yaml
conda activate satmvs
Some packages are list here:
| package | version |
|---|---|
| gdal | 3.3.1 |
| matplotlib | 3.4.3 |
| numpy | 1.12.5 |
| tensorboardx | 2.5 |
| pytorch | 1.4.0 |
| torchvision | 0.5.0 |
| numpy-groupies | 0.9.14 |
| opencv-python | 4.5.5.62 |
Data Preparation
See WHU_TLC/readme.md for more details. And rename the "open_dataset" to "open_dataset_rpc".
Pretrain models
You can download the models at: https://pan.baidu.com/s/1_z_o1ozWryIt7J05l-Rp_w?pwd=xo2p code: xo2p
Train
Train on WHU-TLC dataset using RPC warping:
python train.py --mode="train" --model="red" --geo_model="rpc" --dataset_root=[Your dataset root] --batch_size=1 --min_interval=[GSD(resolution of the image)] --gpu_id="0"
Train on WHU-TLC dataset using homography warping:
python train.py --mode="train" --model="red" --geo_model="pinhole" --dataset_root=[Your dataset root] --batch_size=1 --min_interval=[GSD(resolution of the image)] --gpu_id="0"
Predict
If you want to predict your own dataset, you need to If you want to predict on your own dataset, you need to first organize your dataset into a folder similar to the WHU-TLC dataset. And then run:
python predict.py --model="red" --geo_model="rpc" --dataset_root=[Your dataset] --loadckpt=[A checkpoint]
Citation
If you find this work helpful, please cite our work: @InProceedings{Sat_MVS, author = {Gao, Jian and Liu, Jin and Ji, Shunping},> title = {Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {6148-6157} }
Acknowledgements
Thanks to the authors of UCS-Net, Cas-MVSNet, and VisSat (adapted COLMAP) for open sourcing their fantastic projects. You may want to visit these projects at:
https://github.com/touristCheng/UCSNet