Data obtained by scanning 3D models typically contains missing pieces and holes. These can be caused due to scanning artifacts or artifacts in the surface due to wear and tear. We provide a method based on the Moving Least Squares projection to fill holes in triangular meshes obtained during the process of surface reconstruction . Our method can be applied to holes with non-planar geometry as w...

The Moving Least Squares method (MLS) provides an approximation û of a function u based solely on values u(xj) of u on scattered ”meshless” nodes xj . Derivatives of u are usually approximated by derivatives of û. In contrast to this, we directly estimate derivatives of u from the data, without any detour via derivatives of û. This is a generalized Moving Least Squares technique, and we prove t...

Based on sampling likelihood and feature intensity, in this paper, a feature-preserving denoising algorithm for point-sampled surfaces is proposed. In terms of moving least squares surface, the sampling likelihood for each point on point-sampled surfaces is computed, which measures the probability that a 3D point is located on the sampled surface. Based on the normal tensor voting, the feature ...