Skeleton extraction of 3D objects with radial basis functions

Abstract

A skeleton is a lower dimensional shape description of an object. The requirements of a skeleton differ with applications. For example, object recognition requires skeletons with primitive shape features to make similarity comparison. On the other hand, surface reconstruction needs skeletons, which contain detailed geometry information to reduce the approximation error in the reconstruction process. Whereas many previous works are concerned about skeleton extraction, most of these methods are sensitive to noise, time consuming, or restricted to specific 3D models. A practical approach for extracting skeletons from general 3D models using radial basis functions (RBFs) is proposed. A skeleton generated with this approach conforms more to the human perception. Given a 3D polygonal model, the vertices are regarded as centers for RBF level set construction. Next, a gradient descent algorithm is applied to each vertex to locate the local maxima in the RBF; the gradient is calculated directly from the partial derivatives of the RBF. Finally, with the inherited connectivity from the original model, local maximum pairs are connected with links driven by the active contour model. The skeletonization process is completed when the potential energy of these links is minimized.