Curve-skeletons are a 1D subset of the medial surface of a 3D object and are useful for many visualization tasks including virtual navigation, reduced-model formulation, visualization improvement, mesh repair, animation, etc. There are many algorithms in the literature describing extraction methodologies for different applications; however, it is unclear how general and robust they are. In this paper, we provide an overview of many curve-skeleton applications and compile a set of desired properties of such representations. We also give a taxonomy of methods and analyze the advantages and drawbacks of each class of algorithms.

The usefulness of the 3D Medial Axis (MA) is dependent on both the availability of accurate and stable methods for computing individual MA points and on schemes for deriving the local structure and connectivity among these points. We propose a framework which achieves both by combining the advantages of exact bisector computations used in computational geometry, on the one hand, and the local nature of propagation-based algorithms, on the other, but without the computational complexity, connectivity, added dimensionality, and post processing issues commonly found in these approaches. Specifically, the notion of flow of shocks along the MA manifold is used to identify flow along special points and curves which define a shock scaffold. This 1D scaffold is of lower dimensional complexity than the typical geometric locus of medial points which are represented as 2D sheets. The scaffold not only organizes shape information in a hierarchical manner, but is a tool for the efficient recovery of the scaffold itself and can lead to exact reconstruction. We present examples of this approach for synthetic data, as well as for sherd data from the domain of digital archaeology.

Abstract — A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object’s geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies; 2) extracting curve skeletons through the thinning algorithm; and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the pre-computational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other wellknown methods. Experimental results show advantages when using our method over other techniques.

Representing a 3D shape by a set of one-dimensional curves that are locally symmetric with respect to its boundary (i.e., curve skeletons) is of importance in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, sub-voxel accurate curve skeletons from volumetric objects. A reference point inside the object is considered a point source that transmits two wave fronts of different energies. The first front (β-front) converts the object into a graph, from which the object salient topological nodes are determined. Curve skeletons are tracked from those nodes along the cost field constructed by the second front (α-front) until the point source is reached. The accuracy and robustness of the proposed work are validated against competing techniques as well as a database of 3D objects. Unlike other state-of-the-art techniques, the proposed framework is highly robust because it avoids locating and classifying skeletal junction nodes, employs a new energy that does not form medial surfaces, and finally extracts curve skeletons that correspond to the most prominent parts of the shape, and are hence less sensitive to noise.