Energy-based segmentation of very sparse range surfaces

A segmentation technique for very sparse surfaces is described. It is based on minimizing the energy of the surfaces in the scene. While it could be used in almost any system as part of surface reconstruction/model recovery, the algorithm is designed to be usable when the depth information is scattered and very sparse, as is generally the case with depth generated by stereo algorithms. Results from a sequential algorithm are presented, and a working prototype that executes on the massively parallel Connection Machine is discussed. The technique presented models the surfaces with reproducing kernel-based splines which can be shown to solve a regularized surface reconstruction problem. From the functional form of these splines the authors derive computable upper and lower bounds on the energy of a surface over a given finite region. The computation of the spline, and the corresponding surface representation are quite efficient for very sparse data.<<ETX>>