Growing self-reconstruction maps

Authors:
Renata Lúcia Mendonça Ernesto Do Rêgo;Aluizio Fausto Ribeiro Araújo;Fernando Buarque De Lima Neto
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife, Brazil and Federal Institute of Education, Science and Technology of Pernambuco, Recife, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, Brazil;Department of Computing Systems, Polytechnic School of Engineering, Pernambuco State University, Recife, Brazil
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

In this paper, we propose a new method for surface reconstruction based on growing self-organizing maps (SOMs), called growing self-reconstruction maps (GSRMs). GSRM is an extension of growing neural gas (GNG) that includes the concept of triangular faces in the learning algorithm and additional conditions in order to include and remove connections, so that it can produce a triangular two-manifold mesh representation of a target object given an unstructured point cloud of its surface. The main modifications concern competitive Hebbian learning (CHL), the vertex insertion operation, and the edge removal mechanism. The method proposed is able to learn the geometry and topology of the surface represented in the point cloud and to generate meshes with different resolutions. Experimental results show that the proposed method can produce models that approximate the shape of an object, including its concave regions, boundaries, and holes, if any.