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

We propose Multiscale Correlation searching homogra-phy 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 incur-ring 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. Ac-cording to our experiments, using the above two simple strategies can produce significant homography estimation accuracy with considerable efficiency. We show that MC-Net 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.