2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-8.12
Paper Title FAST AND ACCURATE HOMOGRAPHY ESTIMATION USING EXTENDABLE COMPRESSION NETWORK
Authors Yilei Chen, Guoping Wang, Ping An, Zhixiang You, Xinpeng Huang, Shanghai University, China
SessionMLR-APPL-IP-8: Machine learning for image processing 8
LocationArea E
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.