Growing self-organizing surface map: learning a surface topology from a point cloud.
The growing self-organizing surface map (GSOSM) is a novel map model that learns a folded surface immersed in a 3D space. Starting from a dense point cloud, the surface is reconstructed through an incremental mesh composed of approximately equilateral triangles. Unlike other models such as neural meshes (NM), the GSOSM builds a surface topology while accepting any sequence of sample presentation. The GSOSM model introduces a novel connection learning rule called competitive connection Hebbian learning (CCHL), which produces a complete triangulation. GSOSM reconstructions are accurate and often free of false or overlapping faces. This letter presents and discusses the GSOSM model. It also presents and analyzes a set of results and compares GSOSM with some other models.
Informatics Department, Federal Technology University of Paraná-UTFPR, Medianeira-PR, Brazil. vldmole@utfpr.edu.br
Vilson Luiz
VL
Dalle Mole
Aluizio Fausto Ribeiro
AF
Araújo
D000465:Algorithms; D007858:Learning; D016571:Neural Networks, Computer