• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.85-92
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• 2008

Meshes are the most appropriate data structures for representing 3D geometries. Surface meshes have been frequently used for representing 3D geometries, which only samples data on the surfaces of the given 3D geometries. Thanks to the improvements of computing powers, it is required to develop more complicated contents which utilize the volumetric information of 3D geometries. In this paper, we introduce a novel volumetric mesh libraries based on the half-face data structure, called OpenVolMesh, and describe its designs and implementations. The OpenVolMesh extends the OpenMesh, which is one of the most famous mesh libraries, by supporting volumetric meshes. The OpenVolMesh provides the generic programming, dynamic allocations of primitive properties, efficient array-based data structures, and source-level compatibility with OpenMesh. We show the usefulness of the OpenVolMesh in the developments of 3D volumetric contents with prototypic implementations such as volumetric mesh smoothing and CW-cell decompositions.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.331-342
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• 2004

We study the problem of controlling data overhead of mesh-based multicast ad hoc routing protocols by adaptively adding redundancy to the minimal data overhead multicast mesh as required by the network conditions. We show that the computation of the minimal data overhead multicast mesh is NP-complete, and we propose an heuristic approximation algorithm inspired on epidemic algorithms. In addition, we propose a mobility-aware and adaptive mesh construction algorithm based on a probabilistic path selection being able to adapt the reliability of the multicast mesh to the mobility of the network. Our simulation results show that the proposed approach, when implemented into ODMRP, is able to offer similar performance results and a lower average latency while reducing data overhead between 25 to 50% compared to the original ODMRP.

• Journal of IKEEE
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• v.23 no.4
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• pp.1250-1256
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• 2019

Although one can easily generate real-world 3D mesh data using a 3D printer or a depth camera, the generated data inevitably includes unnecessary noise. Therefore, mesh denoising is essential to obtain intact 3D mesh data. However, conventional mathematical denoising methods require preprocessing and often eliminate some important features of the 3D mesh. To address this problem, this paper proposes a deep learning based 3D mesh denoising method. Specifically, we propose a convolution-based autoencoder model consisting of an encoder and a decoder. The convolution operation applied to the mesh data performs denoising considering the relationship between each vertex constituting the mesh data and the surrounding vertices. When the convolution is completed, a sampling operation is performed to improve the learning speed. Experimental results show that the proposed autoencoder model produces faster and higher quality denoised data than the conventional methods.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.33-41
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• 2009

Triangular mesh models are widely used in reverse engineering, computer graphics, rapid prototyping and NC(numerical controller) tool-path generation. Triangular mesh models are generated from point clouds, surface models and solid models. A halfedge-based triangular mesh data structure is proposed and the development considerations are presented. In the presented data structure, halfedge is the key data structure. Halfedge stores its triangle index and the order in the triangle. Triangles do not store the halfedge lists explicitly. Halfedge is referred by value and defined when it is required. Proposed data structure supports four design requirements: efficient rendering, compact memory, supporting efficient algorithms and easy programming.

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.473-482
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• 2009

Reconstructed 3D data from computer vision includes necessarily a noise or an error. When these data goes through a mesh process, the different 3D mesh data from original shape comes to make by a noise or an error. This paper proposed the method that smooths a noise effectively by noise analysis in reconstructed 3D data. Because the proposed method is smooths a noise using the area ratio of the mesh, the pre-processing of unusable mesh is necessary in 3D mesh data. This study detects a peak noise and Gaussian noise using the ratio of 3D volume and 2D area of mesh and smooths the noise with respect of its characteristics. The experimental results using synthetic and real data demonstrated the efficacy and performance of proposed algorithm.

• International Journal of Contents
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• v.5 no.4
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• pp.1-6
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• 2009

In this paper, we propose a mesh smoothing method for mesh models with noise. The proposed method enables not only the removal of noise from the vertexes but the preservation and smoothing of shape recognized as edges and comers. The magnitude ratio of 2D area and 3D volume in mesh data is adopted for the smoothing of noise. Comparing with previous smoothing methods, this method does not need many iteration of the smoothing process and could preserve the shape of original model. Experimental results demonstrate improved performance of the proposed approach in 3D mesh smoothing.

• International Journal of CAD/CAM
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• v.7 no.1
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• pp.103-112
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• 2007

This paper presents a practical approach for surface reconstruction from unoriented point clouds. Instead of estimating local surface orientation, we first generate a set of depth images from the input point clouds, and a coarse mesh is then generated based on them by space carving techniques. The resultant mesh is progressively refined by local mesh refinement and optimization according to surface distance measure. A manifold mesh approximating the input points within an given tolerance is finally obtained. Our approach is easy to implement, but has the ability to outputs high quality meshes in different resolutions. We show that the proposed approach is not sensitive to several types of data disfigurement and is able to reconstruct models robustly from variance input data.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.984-990
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• 2016

In wireless mesh sensor networks (WMSNs), sensor nodes are connected in the form of a mesh topology and transfer sensor data by multi-hop routing. A data aggregation method for WMSNs is required to minimize the number of routing hops and the energy consumption of each node with limited battery power. This paper presents a shortest path data aggregation method for WMSNs. The proposed method utilizes a simple hash function based on shuffled row major indexing for addressing sensor nodes. This allows sensor data to be aggregated without complex routing tables and calculation for deciding the next hop. The proposed data aggregation algorithms work in a fractal fashion with different mesh sizes. The method repeatedly performs gathering and moves sensor data to sink nodes in higher-level clusters. The proposed method was implemented and simulations were performed to confirm the accuracy of the proposed algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.185-191
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• 2002

In this paper, we propose a 3D object LOD(Level of Detail) modeling system that constructs a mesh from range images and generates the mesh of various LOD using the wavelet transform. In the initial mesh generation, we use the marching cube algorithm. We modify the original algorithm to apply it to construct the mesh from multiple range images efficiently. To get the base mesh we use the decimation algorithm which simplifies a mesh with preserving the topology Finally, when reconstructing new mesh which is similar to initial mesh we calculate the wavelet coefficients by using the wavelet transform. We solve the critical problem of wavelet-based methods - the surface crease problem (1) - by using the mesh simplification as the base mesh generation method.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.586-601
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• 2001

We propose a mesh based multicast routing protocol referred to as HMMRP for ad-hoc networks. In HMMRP, a limited number of sources are selected as core sources, and the rest of the sources of a multicast group are connected to one of those core sources. The sources and the receivers of a multicast group are also connected through per source trees. In HMMRP, the data delivery mesh of a multicast group are composed of the nodes on these paths, and are reconfigured at regular intervals. Furthermore, each mesh member that lies on the paths between the sources and the core sources as well as be-tween the core sources and the receivers keeps checking if there is a symptom of mesh separation around itself. When a mesh member finds such symptom, it tries to patch itself to the mesh with a local flooding. As a result, the part of the data delivery mesh on those paths are kept connected with a lot higher probability than the rest of the data delivery mesh. That is, for a certain source receiver pair, it is very likely that at least there exists a data delivery path that route from the source to a core source and then to the receiver. Therefore, HMMRP may provide very high data delivery ratio without frequent entire data delivery mesh reconfiguration even when the nodal mobility is high. Simulation results show that HMMRP shows relatively little performance degradation with respect to mobility. Furthermore, the performance degradation with respect to mobility is even smaller when the size of the multicast group becomes larger.