Unsupervised Deep Plane-Aware Multi-homography Learning for Image Alignment

Tao Cai, Yunde Jia, Huijun Di*, Yuwei Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to its feature representation capabilities, deep learning has been applied to homography estimation in the field of image alignment. Most deep homography learning methods focus on estimating a single global homography, and cannot deal with the problem of parallax when the scene contains multiple different planes, and the translation of the camera’s optical center is not negligible. In this paper, we propose an unsupervised multi-homography learning method with a plane-perception trait to mitigate this parallax problem. In our model, the problem of multi-homography learning and plane perception are jointly considered, which can benefit from each other. To make the learning process stable under unsupervised setting, we design a special attention mechanism to bootstrap the collaboration between multi-homography learning and plane perception. We construct a new dataset that is captured in real scenes, having many challenges such as multiple planes, large parallax, etc. Quantitative and qualitative results show that our proposed method can better align images with large parallax and multiple planes.

Original languageEnglish
Title of host publicationArtificial Intelligence - 1st CAAI International Conference, CICAI 2021, Proceedings
EditorsLu Fang, Yiran Chen, Guangtao Zhai, Jane Wang, Ruiping Wang, Weisheng Dong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages528-539
Number of pages12
ISBN (Print)9783030930455
DOIs
Publication statusPublished - 2021
Event1st CAAI International Conference on Artificial Intelligence, CICAI 2021 - Hangzhou, China
Duration: 5 Jun 20216 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13069 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st CAAI International Conference on Artificial Intelligence, CICAI 2021
Country/TerritoryChina
CityHangzhou
Period5/06/216/06/21

Keywords

  • Image alignment
  • Multi-homography
  • Plane aware
  • Unsupervised learning

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Cite this

Cai, T., Jia, Y., Di, H., & Wu, Y. (2021). Unsupervised Deep Plane-Aware Multi-homography Learning for Image Alignment. In L. Fang, Y. Chen, G. Zhai, J. Wang, R. Wang, & W. Dong (Eds.), Artificial Intelligence - 1st CAAI International Conference, CICAI 2021, Proceedings (pp. 528-539). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13069 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-93046-2_45