Growing Self-Organizing Maps for Surface Reconstruction from Unstructured Point Clouds

This work introduces a new method for surface reconstruction based on Growing Self-organizing Maps, which learn 3D coordinates of each vertex in a mesh as well as they learn the topology of the input data set. Each map grows incrementally producing meshes of different resolutions, according to the application needs. Another highlight of the presented algorithm refers to the reconstruction time, which is independent from the size of the input data. Experimental results show that the proposed method can produce models that approximate the shape of an object, including its concave regions and holes, if any.