Generalized Multi-camera Scene Reconstruction Using Graph Cuts

Abstract

AbstractReconstructing a 3-D scene from more than one camera is a classical problem in computer vision. One of the major sources of difficulty is the fact that not all scene elements are visible from all cameras. In the last few years, two promising approaches have been developed 11,12 that formulate the scene reconstruction problem in terms of energy minimization, and minimize the energy using graph cuts. These energy minimization approaches treat the input images symmetrically, handle visibility constraints correctly, and allow spatial smoothness to be enforced. However, these algorithm propose different problem formulations, and handle a limited class of smoothness terms. One algorithm 11 uses a problem formulation that is restricted to two-camera stereo, and imposes smoothness between a pair of cameras. The other algorithm 12 can handle an arbitrary number of cameras, but imposes smoothness only with respect to a single camera. In this paper we give a more general energy minimization formulation for the problem, which allows a larger class of spatial smoothness constraints. We show that our formulation includes both of the previous approaches as special cases, as well as permitting new energy functions. Experimental results on real data with ground truth are also included.KeywordsEnergy FunctionVisual HullSmoothness TermScene ReconstructionSpatial SmoothnessThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.