Compact representation of range imaging surfaces

Range images of complex geometry presented by large point data sets almost always yield surface reconstruction imperfections. We propose a novel compact and complete mesh representation for non-uniformly sampled noisy range image data using an adaptive Radial Basis Function network. The network is established using a heuristic learning strategy. Neurons can be inserted, removed or updated iteratively, adapting to the complexity and distribution of the underlying data. This flexibility is particularly suited to highly variable spatial frequencies, and is conducive to data compression with network representations. Experiments confirm the performance advantages of the network when applied to 3D point-cloud surface reconstruction.