Abstract

This paper presents a new class of moves, called ?-expansion- contraction, which generalizes ?-expansion graph cuts for multi-label en- ergy 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 demon- strate 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.