Kernel regression in HRBF networks for surface reconstruction

The Hierarchical Radial Basis Function (HRBF) Network is a neural model that proved its suitability in the surface reconstruction problem. Its non-iterative configuration algorithm requires an estimate of the surface in the centers of the units of the network. In this paper, we analyze the effect of different estimators in training HRBF networks, in terms of accuracy, required units, and computational time.