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DGC-Net: Dense geometric correspondence network

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Details

Original languageEnglish
Title of host publication2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherIEEE
Pages1034-1042
Number of pages9
ISBN (Electronic)9781728119755
DOIs
Publication statusPublished - 4 Mar 2019
Publication typeA4 Article in a conference publication
EventIEEE Winter Conference on Applications of Computer Vision - Waikoloa Village, United States
Duration: 7 Jan 201911 Jan 2019

Publication series

NameIEEE Winter Conference on Applications of Computer Vision
ISSN (Print)1550-5790

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
CountryUnited States
CityWaikoloa Village
Period7/01/1911/01/19

Abstract

This paper addresses the challenge of dense pixel correspondence estimation between two images. This problem is closely related to optical flow estimation task where Con-vNets (CNNs) have recently achieved significant progress. While optical flow methods produce very accurate results for the small pixel translation and limited appearance variation scenarios, they hardly deal with the strong geometric transformations that we consider in this work. In this paper, we propose a coarse-to-fine CNN-based framework that can leverage the advantages of optical flow approaches and extend them to the case of large transformations providing dense and subpixel accurate estimates. It is trained on synthetic transformations and demonstrates very good performance to unseen, realistic, data. Further, we apply our method to the problem of relative camera pose estimation and demonstrate that the model outperforms existing dense approaches.

Publication forum classification

Field of science, Statistics Finland