Using Non Local Features for 3D Shape Grouping

Abstract

Abstract. This work faces the problem of 3D shape clustering when the whole surface information is available. The key of our method is to use a flexible feature, called Cone-Curvature, which provides local and extended information around every node of the mesh that represents the object. Thus as we increase the region around a node a new order of CC can be calculated. This feature, which was originally defined on spherical representation, has been adapted to work with standard triangular meshes and it is used for defining a similarity measure between shapes. Through a PCA technique, the dimensionality of the shape representation is drastically reduced and the hierarchical grouping can be efficiently carried out. This method has been tested under real conditions for a wide set of free shapes yielding promising results. We present a discussion of the clustering comparing human and computer results.

Análisis y Comparación del Descriptor Cone Curvature frente al Reconocimiento de Expresiones Faciales

“…WF en diferentes focos sobre el modelo 3D del rostro (Izquierda). Diferentes frentes de ondas de un nodo N (derecha) [21].…”

A New Segmentation Approach for Old Fractured Pieces

“…Cone curvature is a geometrical feature originally defined and applied on spherical meshes [13] and lately used for clustering purposes in triangular meshes [14].…”