基于Laplace谱嵌入和Mean Shift的 三角网格一致性分割

针对现有网格分割算法对模型姿态及噪声敏感的不足,提出一种基于Laplace谱嵌入和Mean Shift聚类的网格一致性分割算法。采用Laplace-Beltrami算子,将3维空域中的网格模型转化成高维Laplace谱域中的标准型,降低了姿态变化和噪声对分割算法的影响,并增强了网格的结构可分性;在高维谱域中,采用非参数核聚类MeanShift算法,获取模型有视觉意义的语义区域。实验结果表明:该算法可以快速有效地实现具有分支结构三角网格模型的有意义分割且对模型姿态和噪声具有较好的鲁棒性。     

一种基于扩散几何的非刚体三维形状分割方法

三维形状分割是三维形状分析中的一个重要问题.为了使分割结果能适应非刚体丰富的姿态变化,提出一种基于扩散几何的三维网格分割方法.该方法采用波核特征的局部极值点作为非刚体网格模型表面的显著特征点;进而将显著特征点作为初始聚类中心,采用K-均值聚类算法来获得分割结果.实验结果表明,文中方法不仅对处于不同姿态的非刚体三维形状具有良好的分割一致性,而且对噪声、孔洞等具有较好的鲁棒性.     

基于商空间粒度理论的三维网格分割方法

通过研究已有的网格分割和模型简化方法 ,分析三维模型的网格分割中的商空间粒度思想 ,并将商空间粒度计算引入到网格分割中 ,对网格分割过程进行描述 ,提出了基于粒度分层合成技术的网格分割方法。该算法通过分别提取模型中各三角形网格区域的几何特征构成不同的粒度区域 ,再根据粒度合成理论。将这些所形成的粒度组织起来 ,从而实现对三维网格的最终分割 ,为三角网格模型的简化提供了快速有效的方法。实验表明了该算法对于网格分割的有效性和正确性。     

三维网格的Mean Shift并行分割算法

为克服Mean Shift算法复杂度高、速度慢的缺点,提出了一种改进的三维网格模型Mean Shift并行分割算法.该方法基于Mean Shift的基本思想和GPU的高性能并行计算能力,基于离散测地距离的局部极值提取三维网格模型的显著特征点,自显著特征点开始并发执行Mean Shift算法,实现了有意义的分割.与同类算法相比,该算法提高计算运行效率、改善了分割效果.     

基于区域离散曲率的三维网格分水岭分割

针对离散曲率估计对噪声敏感且特征值计算量大的特点提出了基于区域离散曲率的三维网格分水岭分割算法。寻找三维模型显著特征点;对三维模型进行预分割,确定分割带;在分割带区域上计算离散曲度极值点,利用测地距离和曲度极值点对三维模型进行分水岭分割。算法在分割前无需进行网格去噪,实验结果证明,对主体分支明显的模型具有较高的分割边缘准确度和较快的分割速度。     

三维网格分割中聚类分析技术综述

三维网格分割是计算机图形学的一个重要的研究方向,近年来不断涌现出各种新的分割技术.主要关注基于聚类分析的三维网格分割技术,介绍了三维网格分割的2种常见类型,并对分割技术所转化的数学问题进行阐述,总结了一系列常用的网格属性.依据算法类型将现有算法划分为5类,所基于的分割技术分别有区域生长、多源区域生长、层次聚类、迭代聚类以及谱聚类.针对不同的分割目标和所利用的网格属性,对各分类下的分割算法进行对比讨论;同时给出4种角度的评估准则,以展示不同应用场景下各类分割算法的优缺点,并指出了三维网格分割的发展趋势和应用方向.     

基于体半径函数的网格分割算法

为解决现有网格分割算法对表面局部特征和噪声敏感的问题,定义一种刻画模型体属性的测度——体半径,该测度具有姿态不变性和噪声鲁棒性.在此基础上,提出一种网格层次分割算法.计算模型顶点的体半径值,采用二状态高斯混合模型对体半径分布拟合,进行连通分量标注,获取模型的各子部件,结合图切分技术得到有视觉意义的分割结果.实验结果表明...     

变形图驱动和变形感知的谱姿态迁移

为了提高姿态迁移过程中网格表面形状细节的保持能力,减少分层姿态迁移的交互环节,提出变形图驱动且变形感知的自动分层谱姿态迁移方法.首先利用变形图对三维模型进行形状保持的全局低频姿态迁移;然后根据模型变形前后的特征变化自动分割出局部刚性块,并对它再次进行姿态迁移,直到所有局部网格的姿态得到充分迁移.通过多个例子对文中方法和Yin等方法进行了比较,实验结果表明,文中方法次级姿态迁移的次数降低38.0％,平均距离误差降低54.0％,表面积和体积的变化降低12.5％.该方法的姿态迁移较充分,在模型的形状保持上更有优势,且自动化程度较高.     

三角网格模型的最小值边界分割

针对目前网格模型块分割算法综合效果不理想、人工干预多等问题,提出一种基于凹凸信号的最小值边界检测的三角网格模型分割算法.首先通过全局控制顶点的Laplace光顺操作对网格模型进行光顺去噪;然后通过标准化和归一化的凹度信息发现符合人眼视觉的最小值规则的凹特征点;最后结合区域中心线提取算法以及扇形探射线算法构造出闭合的分割线,并用三维主动轮廓模型方法进行优化,通过分割线将模型分割为有意义的分块.实例结果表明,该算法可以快速有效地分割模型,得到有意义的分割结果.     

三维网格模型的分割及应用技术综述

对三维网格模型分割的定义、分类和应用情况做了简要回顾，介绍并评价了几种典型的网格模型分割算法，如分水岭算法、基于拓扑和几何信息的分割算法等；同时，对网格分割在几种典型应用中的研究工作进行了分类介绍和评价．最后对三维分割技术今后的发展方向做出展望．     

A robust confirmable watermarking algorithm for 3D mesh based on manifold harmonics analysis

Owing to the manifold harmonics analysis, a?robust non-blind spectral watermarking algorithm for a?two-manifold mesh is presented, which can be confirmed by a?trusted third party. Derived from the Laplace–Beltrami operator, a?set of orthogonal manifold harmonics basis functions is first adopted to span the spectral space of the underlying three-dimensional (3D) mesh. The minimal number of the basis functions required in the proposed algorithm is also determined, which can effectively accelerate the spectrum computations. Then, to assert ownership and resist 3D mesh forging, a?digital signature algorithm is adopted to sign the watermark in the embedding phase and to verify the signature in the extraction phase, which could optimize the robust non-blind spectral watermarking algorithm framework. To improve the robustness of the embedded watermark signature, the input 3D mesh will be segmented into patches. The watermark signature bits are embedded into the low-frequency spectral coefficients of all patches repeatedly and extracted with regard to the corresponding variations of their coefficients. Extensive experimental results demonstrate the efficiency, invisibility, and robustness of the proposed algorithm. Compared with existing watermarking algorithms, our algorithm exhibits better visual quality and is more robust to resist various geometric and connectivity attacks.     

A triangulation-based hole patching method using differential evolution

In this work, a new hole patching method (namely as, HPDE) is proposed to repair the damaged or ill-scanned three dimensional objects in real engineering applications. Our method differentiates from other related algorithms mainly on the following three aspects. Firstly, our algorithm sufficiently utilizes the point information around the considered hole for each prediction by constructing point correspondences on both sides of the boundary curve of the hole; secondly, the missing points in the hole region are predicted by the algorithm of differential evolution (DE), which is used to obtain the topological and geometrical structures of the mesh in the hole region; thirdly, operations of mesh optimization are adopted for improving the quality of the obtained triangulation mesh. Numerical results on kinds of holes with complex shape and large curvature, and a comparison with two recently proposed algorithms verify the effectiveness of the algorithm, further experiments on the noisy data points illustrate the robustness of the algorithm against noise.     

基于流形调和变换的浅浮雕生成算法

针对利用3D网格生成浅浮雕不能使用精确的网格表示、浮雕细节保持特性依赖于规则采样高度域上分辨率的问题,提出一种基于3D网格频域分析的浅浮雕算法.从信号处理的角度出发,首先利用网格的几何微分性质和拓扑结构通过离散调和变换将网格的深度信号变换到频域;其次提出一种基于几何频率能量的频域划分策略,将频率分割为低频、高频和噪声,再对低频进行线性压缩,利用各项同性的扩散函数对高频进行自适应非线性压缩并过滤噪声,得到信号在频域上一组新的变换系数;最后利用调和逆变换将处理后的频域映射到信号的空域重建浮雕模型.实验结果表明,该算法可通过控制频谱的分割和扩散函数的参数实现浮雕模型细节的增强.     

Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback-Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation-maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion.     

Sparse Approximation of 3D Meshes Using the Spectral Geometry of the Hamiltonian Operator

The discrete Laplace operator is ubiquitous in spectral shape analysis, since its eigenfunctions are provably optimal in representing smooth functions defined on the surface of the shape. Indeed, subspaces defined by its eigenfunctions have been utilized for shape compression, treating the coordinates as smooth functions defined on the given surface. However, surfaces of shapes in nature often contain geometric structures for which the general smoothness assumption may fail to hold. At the other end, some explicit mesh compression algorithms utilize the order by which vertices that represent the surface are traversed, a property which has been ignored in spectral approaches. Here, we incorporate the order of vertices into an operator that defines a novel spectral domain. We propose a method for representing 3D meshes using the spectral geometry of the Hamiltonian operator, integrated within a sparse approximation framework. We adapt the concept of a potential function from quantum physics and incorporate vertex ordering information into the potential, yielding a novel data-dependent operator. The potential function modifies the spectral geometry of the Laplacian to focus on regions with finer details of the given surface. By sparsely encoding the geometry of the shape using the proposed data-dependent basis, we improve compression performance compared to previous results that use the standard Laplacian basis and spectral graph wavelets.     

网格分割的3维网格模型非盲水印算法

目的 信息技术的发展使得面向3维模型版权保护的问题越来越突出,提出一种新的基于网格分割的3维网格模型非盲水印算法。方法 首先使用基于形状直径函数的网格分割算法对3维网格模型进行有意义的网格分割,然后计算每个分块的鲁棒重心并以此为中心将模型由直角坐标系转换到球面坐标系,最后通过调制每个顶点范数的分布来嵌入水印,在水印检测阶段使用非盲检测的方法提取水印。结果 针对目前基于网格分块的水印算法的网格分割不一致以及对分割边界依赖性过强等问题,引入基于形状直径函数的网格分割算法并在重对齐、重采样过程中加入待检测模型与原始模型分块匹配过程以保证网格分割的一致性,并且选取分块的顶点范数的分布作为水印嵌入基元,使得算法能够有效地减弱对分割边界的依赖性。结论 实验结果表明,该算法可以有效抵抗平移、旋转、缩放、噪声、细分、简化、剪切等常见的攻击以及多种攻击的联合攻击。     

A robust feature-preserving semi-regular remeshing method for?triangular meshes

Benefited from the hierarchical representations, 3D models generated by semi-regular remeshing algorithms based on either global parameterization or normal displacement have more advantages for digital geometry processing applications than the ones produced from traditional isotropic remeshing algorithms. Nevertheless, while original models have sharp features or multiple self-intersecting surfaces, it is still a challenge for previous algorithms to produce a semi-regular mesh with sharp features preservation as well as high mesh regularity. Therefore, this study proposes a robust semi-regular remeshing algorithm that uses a two-step surface segmentation scheme to build the high quality base mesh, as well as the regional relationship between the original surface and subdivision domain surface. Using the regional relationship, the proposed algorithm substantially enhances the accuracy and robustness of the backward projection process of subdivision vertices based on normal displacement. Furthermore, the mesh regularity of remeshed models is improved by the quadric mesh relaxation scheme. The experimental results demonstrate the capabilities of the proposed algorithm’s semi-regular remeshing to preserve geometric features and have good triangle aspect ratio.