Contraction Moves for Geometric Model Fitting

Oliver J. Woodford, Minh-Tri Pham, Atsuto Maki, Riccardo Gherardi, Frank Perbet, and Björn Stenger

Abstract. This paper presents a new class of moves, called -expansion-contraction, which generalizes -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.

LNCS 7578, p. 181 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012