MCNet: Rethinking the Core Ingredients for Accurate and Efficient Homography Estimation

Haokai Zhu, Si-Yuan Cao, Jianxin Hu, Sitong Zuo, Beinan Yu, Jiacheng Ying, Junwei Li, Hui-Liang Shen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25932-25941

We propose Multiscale Correlation searching homography estimation Network namely MCNet an iterative deep homography estimation architecture. Different from previous approaches that achieve iterative refinement by correlation searching within a single scale MCNet combines the multiscale strategy with correlation searching incurring nearly ignored computational overhead. Moreover MCNet adopts a Fine-Grained Optimization loss function named FGO loss to further boost the network training at the convergent stage which can improve the estimation accuracy without additional computational overhead. According to our experiments using the above two simple strategies can produce significant homography estimation accuracy with considerable efficiency. We show that MCNet achieves state-of-the-art performance on a variety of datasets including common scene MSCOCO cross-modal scene GoogleEarth and GoogleMap and dynamic scene SPID. Compared to the previous SOTA method 2-scale RHWF our MCNet reduces inference time FLOPs parameter cost and memory cost by 78.9% 73.5% 34.1% and 33.2% respectively while achieving 20.5% (MSCOCO) 43.4% (GoogleEarth) and 41.1% (GoogleMap) mean average corner error (MACE) reduction. Source code is available at https://github.com/zjuzhk/MCNet.