In segmentation for reverse engineering, an input triangular mesh is usually split into segments for each of which a parametric surface is generated. The segment boundaries must be smooth for fine surfaces to be generated, but today's segmentation methods cannot necessarily generate a sufficient level of smoothness. The authors of this paper were inspired by the work reported in, and here try to extend it to efficiently segment a mesh obtained by scanning an object. Fuzzy boundary curves between two neighboring regions are globally rectified using a graph cut approach in which new energy functions are constructed. However, some curves are still not smooth since there are no straight lines around them in the original scanned mesh models, although the corresponding physical curves are smooth. To achieve further smoothing, we split some triangles by adding points and edges based on virtual triangle subdivision and the graph cut approach. As a result, significant further straightening of boundaries is achieved with simple straightest geodesics.

Summary form only given. X-ray CT (computed tomography) has experienced tremendous growth in industrial application in recent years, and acquiring information about the mechanical parts from CT data has been a great challenge for researchers. This paper presents a new method for the registration of a CT volumetric model of an assembly of parts with a CAD mesh model of a part of the assembly using ICP (iterative closest point) registration method. A few steps to extract feature points should be done as a preprocessing step of the volumetric model and the mesh model before applying the ICP registration algorithm, since the volumetric model and the mesh model are different in their data representation. This preprocessing step is important in order to unify the input of the ICP algorithm, and contributes to the robustness and the speed of the registration process.

This paper presents a method for computing distance fields from large volumetric models. Conventional methods have strict limits in terms of the amount of memory space available, as all volumetric models must be allocated to the random access memory (RAM) to compute distance fields. We resolve this problem through an out-of-core strategy. Our algorithm starts by decomposing volumetric models into small regions known as clusters, and distance fields are then computed by Local Distance Transform (LDT) and Inter-Cluster Propagation (ICP). LDT computes the distance transform for each cluster, and since it is independent, other clusters can also be saved to the storage medium. ICP propagates the distance at the boundary of the cluster to neighboring clusters to remove inconsistency in distance fields. In addition, we propose an efficient ordering algorithm based on the propagated distance to reduce LDT and ICP. This paper also demonstrates the results of distance transform from volumetric models with over a billion cells.

The authors propose a novel multiresolution-based shape representation for 3D mesh morphing. Our approach does not use combination operations that caused some serious problems in the previous approaches for mesh morphing. Therefore, we can calculate a hierarchical interpolation mesh robustly using two types of subdivision fitting schemes. Our new representation has a hierarchical semiregular mesh structure based on subdivision connectivity. This leads to various advantages including efficient data storage, and easy acquisition of an interpolation mesh with arbitrary subdivision level. We also demonstrate several new features for 3D morphing using multiresolution interpolation meshes.