• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.13-22
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• 2005

Similarly to the edges defined in a 2D image, we can define the geometric features representing the boundary of the distinctive parts appearing on 3D meshes. The geometric features have been used as basic primitives in several applications such as mesh simplification, mesh deformation, and mesh editing. In this paper, we propose geometric livewire and geometric livelane for extracting geometric features in a 3D mesh, which are the extentions of livewire and livelane methods in images. In these methods, approximate curvatures are adopted to represent the geometric features in a 3D mesh and the 3D mesh itself is represented as a weighted directed graph in which cost functions are defined for the weights of edges. Using a well-known shortest path finding algorithm in the weighted directed graph, we extracted geometric features in the 3D mesh among points selected by a user. In this paper, we also visualize the results obtained from applying the techniques to extracting geometric features in the general meshes modeled after human faces, cows, shoes, and single teeth.

• Journal of the Korea Computer Graphics Society
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• v.7 no.3
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• pp.9-18
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• 2001

Feature detection is important in various mesh processing techniques, such as mesh editing, mesh morphing, mesh compression, and mesh signal processing. In this paper, we propose a geometric snake as an interactive tool for feature detection on a 3D triangular mesh. A geometric snake is an extension of an image snake, which is an active contour model that slithers from its initial position specified by the user to a nearby feature while minimizing an energy functional. To constrain the movement of a geometric snake onto the surface of a mesh, we use the parameterization of the surrounding region of a geometric snake. Although the definition of a feature may vary among applications, we use the normal changes of faces to detect features on a mesh.

• Korean Journal of Computational Design and Engineering
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• v.9 no.4
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• pp.294-305
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• 2004

Highly detailed geometric models are rapidly becoming commonplace in computer graphics and other applications. These complex models, which is often represented as complex1 triangle meshes, mainly suffer from the vast memory requirement for real-time manipulation of arbitrary geometric shapes without loss of data. Various techniques have been devised to challenge these problems in views of geometric processing, not a representation scheme. This paper proposes the new mesh structure for the compact representation and the efficient handling of the highly complex models. To verify the compactness and the efficiency, the memory requirement of our representation is first investigated and compared with other existing representations. And then we analyze the time complexity of our data structure by the most critical operation, that is, the enumeration of the so-called one-ring neighborhood of a vertex. Finally, we evaluate some elementary modeling functions such as mesh smoothing, simplification, and subdivision, which is to demonstrate the effectiveness and robustness of our mesh structure in the context of the geometric modeling and processing.

• International Journal of Contents
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• v.3 no.2
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• pp.25-29
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• 2007

In this paper, we discuss the mesh segmentation problem which divides a given 3D mesh into several disjoint sets. To solve the problem, we propose a greedy method based on the merging priority metric defined for representing the geometric properties of meaningful parts. The proposed priority metric is a weighted function using five geometric parameters, those are, a distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. In special, we can control by setting up the weight values of the proposed geometric parameters to obtain visually better mesh segmentation. Finally, we carry out an experiment on several 3D mesh models using the proposed methods and visualize the results.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.365-372
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• 2004

Recently, the studies for mesh simplification have been increased according to the application area of the complicate 3D mesh models has been expanded. This paper introduces a novel method for mesh simplification which uses the properties of the mesh model in addition to the geometric locations of the model. The information of the 3D mesh model Includes surface properties such as color, texture, and curvature information as well as geometic information of the model. The most of current simplification methods adopt such geometric information and surface properties individually for mesh simplification. However, the proposed simplification method combines the geometric information and solace properties and applies them to the simplification process simultaneously. In this paper, we exploit the extended geometry based quadric error metric(QEM) which relatively allows fast and accurate geometric simplification of mesh. Thus, the proposed mesh simplification utilizes the quadric error metric based on geometric information and the surface properties such as color, normal, and texture. The proposed mesh simplification method can be expressed as a simple quadric equation which expands the quadric error metric based on geometric information by adding surface properties such as color, normal, and texture. From the experimental results, the simplification of the mesh model based on the proposed method shows the high fidelity to original model in some respects such as global appearance rather than using current geometry based simplification.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.848-853
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• 2005

3D geometric modeling of an object of interest has been intensively investigated in many fields including CAD/CAM and computer graphics. Traditionally, CAD and geometric modeling tools are widely used to create geometric models that have nearly the same shape of 3D real objects or satisfy designers intent. Recently, with the help of the reverse engineering (RE) technology, we can easily acquire 3D point data from the objects and create 3D geometric models that perfectly fit the scanned data more easily and fast. In this paper, we present 3D geometric model generation based on a stereo vision system (SVS) using random pattern projection. A triangular mesh is considered as the resulting geometric model. In order to obtain reasonable results with the SVS-based geometric model generation, we deal with many steps including camera calibration, stereo matching, scanning from multiple views, noise handling, registration, and triangular mesh generation. To acquire reliable stere matching, we project random patterns onto the object. With experiments using various random patterns, we propose several tips helpful for the quality of the results. Some examples are given to show their usefulness.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.462-465
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• 2000

It is important to compress three dimensional (3D) data efficiently, since 3D data are too large to store or transmit in general. In this paper, we propose a lossless compression algorithm of the 3D mesh connectivity, based on the vertex degree. Most techniques for the 3D mesh compression treat the connectivity and the geometric separately, but our approach attempts to exploit the geometric information for compressing the connectivity information. We use the geometric angle constraint of the vertex fanout pattern to predict the vertex degree, so the proposed algorithm yields higher compression efficiency than the conventional algorithms.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.331-334
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• 2003

In the finite element simulation of hot forging processes using hexahedron, remeshing of a flash is very difficult. The mesh compression method is a remeshing technique to construct an effective hexahedral mesh. However, because mesh is distorted during the compression procedure or the mesh compression method, mesh smoothing is necessary to improve the mesh Qualify. in this study, several geometric mesh smoothing techniques and a matrix norm optimization technique are applied and compared which is more adaptive to the mesh compression method.

• Journal of Korean Association for Spatial Structures
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• v.10 no.2
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• pp.77-86
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• 2010

This paper provides background theories and numerical results of automatic finite element (FE) mesh generation for freeform discrete structures. The present method adopts the computer aided geometric design (CAGD) technique to overcome the limitation of case-sensitive traditional automatic FE mesh generator. The present technique involves two steps. The first one is to represent the shape of the structure using the geometric model based on the CAGD and the second one is to generate the discrete FE mesh of spatial structures over the geometric model. From numerical results, it is found to be that the present technique is very easy to produce the FE mesh for free-form spatial structures and it can also reuse some features of traditional automatic mesh generator in the process. Furthermore, it shows the possibility to be used for the shape optimization of large spatial structures.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.435-442
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• 2007

This paper is concerned with the mesh segmentation problem that can be applied to diverse applications such as texture mapping, simplification, morphing, compression, and shape matching for 3D mesh models. The mesh segmentation is the process of dividing a given mesh into the disjoint set of sub-meshes. We propose a method for segmenting meshes by simultaneously reflecting global and local geometric characteristics of the meshes. First, we extract sharp vertices over mesh vertices by interpreting the curvatures and convexity of a given mesh, which are respectively contained in the local and global geometric characteristics of the mesh. Next, we partition the sharp vertices into the $\kappa$ number of clusters by adopting the $\kappa$-means clustering method [29] based on the Euclidean distances between all pairs of the sharp vertices. Other vertices excluding the sharp vertices are merged into the nearest clusters by Euclidean distances. Also we implement the proposed method and visualize its experimental results on several 3D mesh models.