Learning to Generate High-Quality Images for Homography Estimation.


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Nowadays, robots have gradually replaced humans to perform tasks in challenging environments, but visually satisfactory images may not always contain high-quality features required by downstream algorithms. In this paper, we reveal that in the homography estimation, which is the essential task of robot vision, algorithms show different sensitivities to aerial images and sometimes perform better in over/under-exposed scenes. To this point, we utilize the gamma correction theory to design an interpretable exposure adjustment function, and train a personalized exposure selection network ESH-Net by contrastive learning. After adjusting the exposure of original input by our method, algorithms can obtain better homography results. Comprehensive experiments are conducted on samples generated from the aerial DOTA dataset. Results show that our method can produce high-quality images for both learning-based and traditional homography estimators, and outperforms other exposure enhancement networks.
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Key words
Homography estimation, exposure adjustment, contrastive learning
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