The majority of the existing techniques for surface reconstruction and the closely related
problem of normal reconstruction are deterministic. Their main advantages are the speed …

This paper presents optimization techniques that substantially speed up the Growing Neural
Gas (GNG) algorithm. The GNG is an example of the Self-Organizing Map algorithm that is a …

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 …

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 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 …

This work proposes the use of neural network ensembles to boost the performance of a
neural network based surface reconstruction algorithm. Ensemble is a very popular and …

Surface reconstruction from a point cloud is very useful in many different application areas,
such as manufacturing (Bernardini et al., 1999), cultural heritage (Bernardini et al., 2002) …

With the explosion of multimedia content and its ease of distribution over Internet, there is a
need for copyright protection of digital content. Whether it is music albums swapped over …

We propose a surface reconstruction method based on self-organizing maps that grows
incrementally. The method, a variant of a previously proposed method called Growing Self …

We describe a Learning algorithm for surface reconstruction based on an incrementally
expanding Neural Network known as Growing Cell Structure. The Neural Network (Neural …