MCNet: Rethinking the Core Ingredients for Accurate and Efficient Homography Estimation
Haokai Zhu, Siyuan Cao, Jianxin Hu, Sitong Zuo, Beinan Yu, Jiacheng Ying, Junwei Li, Hui‐Liang Shen
Abstract
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.