A self-organizing algorithm for multisensory surface reconstruction

In this paper a method is presented to reconstruct and model an unknown three-dimensional surface which is described by an unordered cloud of sampled surface points. For that purpose Kohonen's self-organizing feature map is modified accordingly. This algorithm models the 2-D subspace in the 3-D input space by defining the appropriate parameter grid. In the second part of the paper the authors discuss the extensions of Kohonen's algorithm, which were necessary to handle multiple sensor input, addressing orientation discontinuities and defining the reconstruction resolution according to surface properties. The result is a method which can perform satisfactorily on sparse as well as on dense input data. Because the surface is described in a parameterized form viewpoint independence is inherent. In the last part experiments with real and simulated data are presented, in which the ROTEX telerobotic station served as background scenario.<<ETX>>