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