We propose a novel unsupervised cross-modal homography estimation framework based on intra-modal Self-supervised learning, Correlation, and consistent feature map Projection, namely SCPNet. The concept of intra-modal self-supervised learning is first presented to facilitate the unsupervised cross-modal homography estimation. The correlation-based homography estimation network and the consistent feature map projection are combined to form the learnable architecture of SCPNet, boosting the unsupervised learning framework. SCPNet is the first to achieve effective unsupervised homography estimation on the satellite-map image pair cross-modal dataset, GoogleMap, under [-32,+32] offset on a 128 × 128 image, leading the supervised approach MHN by 14.0% of mean average corner error (MACE). We further conduct extensive experiments on several cross-modal/spectral and manually-made inconsistent datasets, on which SCPNet achieves the state-of-the-art (SOTA) performance among unsupervised approaches, and owns 49.0%, 25.2%, 36.4%, and 10.7% lower MACEs than the supervised approach MHN.
Evaluation on the GoogleMap dataset:
python test_SCPNet.py --model_dir ./ckpt/ --model_name ggmap.pth --dataset ggmap
Training on the GoogleMap dataset:
python train_SCPNet.py --model_dir ./result/exp_ggmap --dataset ggmap
This project is released under the Apache 2.0 license.
If you have any other problems, feel free to post questions in the issues section or contact Runmin Zhang (runmin_zhang@zju.edu.cn) and Si-Yuan Cao (cao_siyuan@zju.edu.cn).
This work is mainly based on RAFT and IHN, we thank the authors for the contribution.