Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint

• URL: http://arxiv.org/abs/2212.02763v1
• Date: Tue, 6 Dec 2022 05:28:05 GMT
• Title: Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint
• Authors: Hai Jiang, Haipeng Li, Yuhang Lu, Songchen Han, and Shuaicheng Liu
• Abstract summary: Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. We propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones. Our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes.
• Score: 25.022907946911033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.

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