SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization
This is the implementation of the paper "SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization"
- Create a new anaconda environment and install all required packages before running the code.
conda create --name SSHNet python=3.9
conda activate SSHNet
pip install -r requirements.txt
# SSHNet
python -u train.py --gpuid 0 --dataset ggmap --note exp
# SSHNet-D
python -u train_distillation.py --gpuid 0 --dataset ggmap --checkpoint ./logs/optsar/model_iter_120000 --note exp
python -u train.py --gpuid 0 --mode test --dataset ggmap --checkpoint ./logs/optsar/model_iter_120000 --note test
This project is released under the Apache 2.0 license.
Junchen Yu: yujc@zju.edu.cn
Si-Yuan Cao: cao_siyuan@zju.edu.cn