Optical Flow Estimation Using Laplacian Mesh Energy
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.
Cite
Text
Li et al. "Optical Flow Estimation Using Laplacian Mesh Energy." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.315Markdown
[Li et al. "Optical Flow Estimation Using Laplacian Mesh Energy." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/li2013cvpr-optical/) doi:10.1109/CVPR.2013.315BibTeX
@inproceedings{li2013cvpr-optical,
title = {{Optical Flow Estimation Using Laplacian Mesh Energy}},
author = {Li, Wenbin and Cosker, Darren and Brown, Matthew and Tang, Rui},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2013},
doi = {10.1109/CVPR.2013.315},
url = {https://mlanthology.org/cvpr/2013/li2013cvpr-optical/}
}