Pytorch implementation of Promoting Single-Modal Optical Flow Network for Diverse Cross-modal Flow Estimation (AAAI 2022)
The model can be used as a powerful zero-shot multimodal image matching/registration baseline.
Download the pre-trained model, and put it in the 'pre_trained' folder.
baidu yun access code: sztg
You can check 'run_model.py' for detailed usage.
Prepare the corresponding dataset and modify the path in 'evaluate_dataset.py'.
Cross-KITTI access code: sztg
Then run evaluate_for_crossKitti.py / evaluate_for_rgbnir_stereo.py / evaluate_for_trimodalhuman.py.
Prepare the dataset and modify the path in 'dataset.py'.
Run 'train.py' to train.
下载预训练模型,并放入pre_trained文件夹: 百度云 提取码 sztg
参考run_model.py的用法
准备相应的数据集,修改evaluate_dataset.py中的路径
Cross-KITTI 提取码 sztg
运行evaluate_for_crossKitti.py / evaluate_for_rgbnir_stereo.py / evaluate_for_trimodalhuman.py
准备数据集,并修改dataset.py中的数据集路径
运行train.py进行训练