Distance Based Feature Detection on 3D Point Sets

Using the Voronoi diagram of the input point set, we deduce a tensor field whose principal axes and eccentricities locally represent respectively the most likely direction of the

The Distance Hierarchy (DH) algorithm adopts a Bounding Volume Hierarchical approach and a feature-based method to obtain a simple and fast technique for collision detection..

The algorithm proposed in this paper is also an implicit SOM surface recon- struction, however, in a major difference with [YIL08] and all the other previously proposed SOM methods,

It consists of shape histograms which reflect the geodesic distance distribution of randomly chosen pairs of surface points as well as the distribution of geodesic eccentricity

Construct surface in steps Vary topological constraint u = umbrella count at p in hull umbrella = incident edge-pair -> minimize boundary length Boundary Complex is sub-set

Further future work includes using methods for an auto- matic 3D reconstruction of fragments based on conclusions of the script feature analysis, virtual fragment synthesis for

This paper introduces an enhanced 3D object retrieval technique using a compact and highly discriminative feature point descriptor.. The key idea is based on integrating Bag of

We present a novel approach towards mesh saliency detection, based on local curvature entropy.. Being based on information the- ory, our method classifies regions on a 3D