Optical Flow Estimation Using Laplacian Mesh Energy

Wenbin Li, Darren Cosker, Matthew Brown, Rui Tang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2435-2442

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.

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[bibtex]
@InProceedings{Li_2013_CVPR,
author = {Li, Wenbin and Cosker, Darren and Brown, Matthew and Tang, Rui},
title = {Optical Flow Estimation Using Laplacian Mesh Energy},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}