Optical flow estimation using Laplacian Mesh Energy

Wenbin Li, Darren Cosker, Matthew Brown, Rui Tang

Research output: Contribution to conferencePaper

16 Citations (Scopus)


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
Number of pages8
Publication statusPublished - 30 Jun 2013
EventIEEE International Conference on Computer Vision and Pattern Recognition (CVPR) - Oregon, US, UK United Kingdom
Duration: 25 Jun 201327 Jun 2013


ConferenceIEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
CountryUK United Kingdom
CityOregon, US

Fingerprint Dive into the research topics of 'Optical flow estimation using Laplacian Mesh Energy'. Together they form a unique fingerprint.

Cite this