Optical flow estimation using Laplacian Mesh Energy

Wenbin Li, Darren Cosker, Matthew Brown, Rui Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration. The algorithm uses a unique Laplacian Mesh Energy term to encourage local smoothness whilst simultaneously preserving non-rigid deformation. Laplacian deformation approaches have become popular in graphics research as they enable mesh deformations to preserve local surface shape. In this work we propose a novel Laplacian Mesh Energy formula to ensure such sensible local deformations between image pairs. We express this wholly within the optical flow optimization, and show its application in a novel coarse-to-fine pyramidal approach. Our algorithm achieves the state-of-the-art performance in all trials on the Garg et al. dataset, and top tier performance on the Middlebury evaluation.
Original languageEnglish
Title of host publication2013 IEEE Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages2435-2442
Number of pages8
ISBN (Electronic)978-1-5386-5672-3
DOIs
Publication statusPublished - 3 Oct 2013
EventIEEE International Conference on Computer Vision and Pattern Recognition (CVPR) - Oregon, US, UK United Kingdom
Duration: 25 Jun 201327 Jun 2013

Publication series

NameIEEE Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Volume2013
ISSN (Print)1063-6919
ISSN (Electronic)1063-6919

Conference

ConferenceIEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Country/TerritoryUK United Kingdom
CityOregon, US
Period25/06/1327/06/13

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