Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
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This paper develops an unsupervised learning algorithm that trains a Deep Convolutional Neural Network to estimate planar homographies. The traditional feature-based approaches to estimating can fail when good features cannot be identified, and can be slow during the feature identification and matching process. We demonstrate that our unsupervised algorithm outperforms these traditional approaches in accuracy, speed, and robustness to noise on both synthetic images and real-world images captured from a UAV. In addition, we demonstrate that our unsupervised method has superior performance compared to the corresponding supervised method of training the network architecture using ground truth homography labels. Our approach is a general motion estimation framework that can be extended beyond estimating planar homographies to more general motions such as optical flow.
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