Contraction Moves for Geometric Model Fitting
Abstract
This paper presents a new class of moves, called α-expansion-contraction , which generalizes α -expansion graph cuts for multi-label energy minimization problems. The new moves are particularly useful for optimizing the assignments in model fitting frameworks whose energies include Label Cost (LC), as well as Markov Random Field (MRF) terms. These problems benefit from the contraction moves’ greater scope for removing instances from the model, reducing label costs. We demonstrate this effect on the problem of fitting sets of geometric primitives to point cloud data, including real-world point clouds containing millions of points, obtained by multi-view reconstruction.
Cite
Text
Woodford et al. "Contraction Moves for Geometric Model Fitting." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33786-4_14Markdown
[Woodford et al. "Contraction Moves for Geometric Model Fitting." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/woodford2012eccv-contraction/) doi:10.1007/978-3-642-33786-4_14BibTeX
@inproceedings{woodford2012eccv-contraction,
title = {{Contraction Moves for Geometric Model Fitting}},
author = {Woodford, Oliver J. and Pham, Minh-Tri and Maki, Atsuto and Gherardi, Riccardo and Perbet, Frank and Stenger, Björn},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {181-194},
doi = {10.1007/978-3-642-33786-4_14},
url = {https://mlanthology.org/eccv/2012/woodford2012eccv-contraction/}
}