Fast And Accurate Homography Estimation Using Extendable Compression Network

Fast and accurate homography estimation between images is crucial for relative pose estimation in autonomous exploration. Recently, learning-based methods have been proposed to use semantic information to solve challenging cases like large displacements, dynamic scenes, and illumination changes, where traditional methods may degrade. However, most existing methods have large model sizes and low inference speed, which make them infeasible in terminal devices and real-time scenarios. In this paper, we build a basic network based on the ShuffleNetV2 compressed units, which can extremely accelerate the homography estimation process. To further deal with the large displacements, we extend the basic network to a multiscale weight-shared form to additionally process the half-scale input. In the case of sufficient computational resources, this basic network can also be extended to a recurrent coarse-to-fine form to achieve the most accurate results. Experimental results show that our extendable networks can well balance the accuracy and inference speed, and the sizes of all models are less than 9MB.