In recent years, many new methods have been proposed for extracting curve skeletons of 3D shapes, using a mesh-contraction principle. However, it is still unclear how these methods perform with respect to each other, and with respect to earlier voxel-based skeletonization methods, from the viewpoint of certain quality criteria known from the literature. In this study, we compare six recent contraction-based curveskeletonization methods that use a mesh representation against six accepted quality criteria, on a set of complex 3D shapes. Our results reveal previously unknown limitations of the compared methods, and link these limitations to algorithmic aspects of the studied methods.

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Abstract:Computing curve skeletons of 3D shapes is a challenging task. Recently, a high-potential technique for this task was proposed, based on integrating medial information obtained from several 2D projections of a 3D shape (Livesu et al., 2012). However effective, this technique is strongly influenced in terms of complexity by the quality of a so-called skeleton probability volume, which encodes potential 3D curve-skeleton locations.In this paper, we extend the above method to deliver a highly accurate and discriminative curve-skeleton probability volume. For this, we analyze the error sources of the original technique, and propose improvements in terms of accuracy, culling false positives, and speed. We show that our technique can deliver point-cloud curve-skeletons which are close to the desired locations, even in the absence of complex postprocessing. We demonstrate our technique on several 3D models.