基于曲度特征的三维模型检索算法

针对如何提高复杂曲面的三维模型的检索精度的问题,提出了一种基于曲度特征的三维模型检索算法。首先,在模型表面选取随机采样点,计算点所在局部曲面的高斯曲率和平均曲率,通过高斯曲率和平均曲率求出随机点的曲度值,曲度值表明了曲面的凹凸属性。然后,以模型的质心为球心,以随机点与质心距离和曲度值为坐标轴建立坐标系,统计出一定距离范围内曲度值分布的概率,构建距离与曲度的分布矩阵,以此分布矩阵作为三维模型特征描述符。该特征描述符具有旋转不变性和平移不变性,能够很好地反映复杂曲面的几何特征。最后,通过比较分布矩阵给出不同模型间的相似度。实验结果表明,该方法相比形状分布算法的检索性能有较大提高,尤其适用于具有复杂曲面的三维模型检索。     

大气紊流的数字仿真

为提高大气紊流数字模拟的精度,分析Dryden紊流模型的数字仿真技术,发现仿真中采用的高斯白噪声序列的生成精度是影响大气紊流模拟精度的重要因素.在不改变随机数序列均值、方差以及概率密度特性的情况下,采用双随机交换最小化方法对高斯噪声序列进行白化处理,使得随机数序列的白化程度提高,从而使紊流模拟的精度提高,而且模拟精度受采样间隔的影响降低.采用双随机交换最小化方法对高斯白噪声序列进行改进是提高紊流模拟精度的1种直接有效的手段.     

一种基于人工免疫聚类的三维模型检索算法

为提高对三维模型检索的性能,通过借鉴生物免疫系统中的克隆选择原理和记忆机制,将一种具有更快收敛速度和更高收敛精度的人工免疫K-Means混合聚类算法应用于三维模型对称特性提取上,经过采样、计算得出三维模型的实际对称平面,以此作为特征信息进行三维模型的检索。对比实验表明,该方法可以获得较优的检索性能。     

改进的三维模型形状分布检索算法

针对传统D1距离形状分布函数获取采样点计算复杂、模型内容描述不充分和检索速率低下等问题提出了一种改进方法。该方法的关键点是：首先采用平移和缩放对模型进行标准化处理,用于减少面片之间的差异,使得采样点均匀地落在模型的表面;其次采用三角面片的索引号进行随机数的生成,并且利用三角面片的重心和质心进行有效的计算,以便用于缩短模型的处理时间和提高检索速率。利用普林斯顿大学三维模型数据库中的部分模型作为实验数据,实现结果表明：改进的方法不会降低模型的检索性能,并有效地减少了模型查询和处理时间。     

基于表面颜色属性的三维模型检索

对基于表面属性的三维模型检索进行研究,提出了基于面积颜色分布、基于球面颜色分布、基于颜色球面调和的三维模型检索方法以及基于表面属性的融合方法.通过对模型表面进行按面积均匀采样来获取其颜色分布信息后形成基于面积颜色分布的特征.通过对模型进行均匀球面采样获取颜色分布信息形成基于球面颜色分布的特征.通过首先将模型表面颜色信息按等经纬度球面采样映射为球面函数,然后对其进行球面调和变换形成颜色球面调和特征.融合特征通过对以上特征的两组或3组依一定权值组合而得到.为验证基于表面属性的三维模型检索方法的效果,建立了一个包含有1171个颜色模型的测试库.实验结果表明以上方法均对基于表面属性的三维模型检索具有较好的检索效果且融合特征可以得到优于任何单一特征的检索效果.     

基于点对分布的三维模型特征提取算法

基于形状分布算法提出了一种基于点对分布的三维模型特征提取算法。该算法根据两随机点间的连线与模型是否有交点，将连线分为U类和W类，并分别为两类连线构建形状分布直方图，从而解决了形状分布算法对于外形相近但细节不同的三维模型的检索问题，提高了检索精度。     

基于面积分布算子的三维模型检索算法

在产品设计、3D制作等领域中经常使用大量的三维模型,但现有的基于属性的检索方式由于属性数据不全、不准的问题,搜索引擎还不提供有效的三维模型检索服务。提出了一种与CAD系统无关的基于面积分布算子的三维模型检索算法,该算法采用模型中顶点关联的三角形面积总和及平均面积构成的面积序列,对面积分布序列进行归一化操作和傅里叶变换等处理,得到最终的面积分布算子,将三维模型的相似度计算映射为面积分布序列的比较。在普渡大学建立的工程标准模型库(ESB)上对该算法进行了验证和比较。实验结果表明该算法客观反映了模型的相似程度,明显提高了传统的检索算法的效率和速度。     

基于聚类的三维模型检索算法

将聚类算法应用于三雏模型对称特性的提取上,实现三维模型的检索.对三维模型表面进行采样,根据采样点计算三维模型的对称平面,通过聚类算法得出三维模型实际的对称平面,以此对称特性作为三维模型的特征信息,进行三维模型的检索.     

均匀分割主曲线的三维形状描述

将无序的、分布不均匀的三维网格模型形状分布问题，转换为有序的、均匀的一维流形问题，进而实现三维模型的形状检索．本文将三维网格模型，投影为三维的光顺主曲线；通过对主曲线沿弧长方向的均匀分割采样，提取主曲线的空间位置和姿态等形状特征；最后通过形状描述向量的比较实现了对应的三维模型检索．实验证明该形状描述方法对几何变换是稳定的，降低了多分辨率、局部形变、噪声等因素影响对三维网格模型形状描述的干扰．     

模型驱动下的形状分布算法

三维模型的特征提取方法中,形状分布算法简单,具有强的不变性和鲁棒性,但其抽样点不具有代表性,影响了系统的检索精度.为增强抽样点的有效性,我们改进抽样策略,首先引入模型复杂度描述参数,扩大抽样点集;然后利用可分离离散小波变换,逐层分解样点集,增强其有效性,并使最终样点数相同;最后使用形状分布算法进行三维模型的特征提取,并应用在三维模型检索上,对比实验结果表明,改进后的算法提高了系统的检索精度.     

Enhancement of sparse 3D reconstruction using a modified match propagation based on particle swarm optimization

Sparse 3D reconstruction, based on interest points detection and matching, does not allow to obtain a suitable 3D surface reconstruction because of its incapacity to recover a cloud of well distributed 3D points on the surface of objects/scenes. In this work, we present a new approach to retrieve a 3D point cloud that leads to a 3D surface model of quality and in a suitable time. First of all, our method uses the structure from motion approach to retrieve a set of 3D points (which correspond to matched interest points). After that, we proposed an algorithm, based on the match propagation and the use of particle swarm optimization (PSO), which significantly increases the number of matches and to have a regular distribution of these matches. It takes as input the obtained matches, their corresponding 3D points and the camera parameters. Afterwards, at each time, a match of best ZNCC value is selected and a set of these neighboring points is defined. The point corresponding to a neighboring point and its 3D coordinates are recovered by the minimization of a nonlinear cost function by the use of PSO algorithm respecting the constraint of photo-consistency. Experimental results show the feasibility and efficiency of the proposed approach.

     

三维CAD曲面模型距离-曲率形状分布检索算法

产品三维CAD模型检索对实现产品设计信息的有效重用具有重要意义和作用.针对在工程应用中存在大量含有曲面的产品,提出一种基于距离-曲率形状分布的三维CAD曲面模型检索算法.首先在模型表面随机取点,计算出所取点的曲率以及该点到模型质心的距离;然后以模型质心为球心,统计处在不同半径球壳间所取随机点的曲率分布,分别以球壳半径和曲率为坐标轴构建距离-曲率平面网格,并统计每个网格中随机点出现的频次,从而形成距离-曲率矩阵;最后利用特定的规则计算矩阵的相似值,用该值衡量2个模型的相似程度,实现模型的相似性检索.实验结果表明,文中算法的检索性能比传统的形状分布算法有较大提高,尤其适用于自由曲面模型的检索.     

基于拟蒙特卡罗方法的三维医学重建模型体积测量方法研究

测量医学图像三维重建病灶组织与器官的体积, 为临床诊疗与医学研究提供更可靠的数据。通过先对系列二维医学图像进行预处理, 并对处理后的图像进行三维重建, 可以获得较好的只有表面三角网格的三维重建模型; 然后使用拟蒙特卡罗方法在构造的包围盒内生成低差异分布的随机点, 通过计算模型内的点数量与全部随机点数量的比例进行体积测量。分别对四组重建的三维模型进行体积测量并与蒙特卡罗方法相比, 拟蒙特卡罗方法在测量体积方面具有较好的效果。拟蒙特卡罗方法对三维医学重建模型体积测量可以得到较准确的体积数据, 具有一定的实际应用与理论研究价值。     

基于扫描激光测距数据的建筑物三维重建

建筑物的三维信息是重要的基础地理信息,可以应用于城市的规划、管理等方面,而机载激光扫描测距系统在城市建筑物的三维重建方面具有重要的应用价值。针对我国自行研制的机载激光扫描测距数据提出了一套利用稀疏激光测距数据自动提取建筑物三维信息的方法。首先根据测量的激光距离和相应的姿态、位置信息计算出激光采样点的三维位置,进而可以生成城市的数字表面模型(DSM)和城市的数字地面模型(DTM),依据它们能准确地提取出建筑物的轮廓信息,最后根据建筑物具有规则的形状这个特点来对建筑物的轮廓线进行规则化处理而最终恢复建筑物的三维信息。通过对北京城市北部地区的实际处理,说明了该方法的可行性。     

Progressive Multi‐Jittered Sample Sequences

We introduce three new families of stochastic algorithms to generate progressive 2D sample point sequences. This opens a general framework that researchers and practitioners may find useful when developing future sample sequences. Our best sequences have the same low sampling error as the best known sequence (a particular randomization of the Sobol’ (0,2) sequence). The sample points are generated using a simple, diagonally alternating strategy that progressively fills in holes in increasingly fine stratifications. The sequences are progressive (hierarchical): any prefix is well distributed, making them suitable for incremental rendering and adaptive sampling. The first sample family is only jittered in 2D; we call it progressive jittered. It is nearly identical to existing sample sequences. The second family is multi‐jittered: the samples are stratified in both 1D and 2D; we call it progressive multi‐jittered. The third family is stratified in all elementary intervals in base 2, hence we call it progressive multi‐jittered (0,2). We compare sampling error and convergence of our sequences with uniform random, best candidates, randomized quasi‐random sequences (Halton and Sobol'), Ahmed's ART sequences, and Perrier's LDBN sequences. We test the sequences on function integration and in two settings that are typical for computer graphics: pixel sampling and area light sampling. Within this new framework we present variations that generate visually pleasing samples with blue noise spectra, and well‐stratified interleaved multi‐class samples; we also suggest possible future variations.     

三维模型两点连通概率函数的快速计算

三维数字岩心模型的连通性分析中,两点连通概率函数是一个重要的统计参数。针对该函数由于计算复杂度过于庞大而造成计算时间过长的问题,提出了使用基于均匀分布的随机函数的空间随机采样法,缩小待计算数据集的规模,同时进行并行优化,达到缩短计算时间的目的。通过实验对比,结果表明,这种采样方法在平均相对误差较小的情况下,能够达到缩短计算时间数百倍,具有良好的稳定性。     

Using low-discrepancy sequences and the Crofton formula to compute surface areas of geometric models

The surface area of a geometric model, like its volume, is an important integral property that needs to be evaluated frequently and accurately in practice. In this paper, we present a new quasi-Monte Carlo method using low-discrepancy sequences for computing the surface area of a 3D object. We show that the new method is more efficient than a Monte Carlo method using pseudo-random numbers. This method is based on the Cauchy-Crofton formula from integral geometry, and it computes the surface area of a 3D body B by counting the number of intersection points between the boundary surface of B and a set of straight lines in E³. Low discrepancy sequences are used to generate the set of lines in E³ to reduce the estimation errors that would be caused by using statistically uniformly distributed lines. We study and compare two different methods for generating 3D random lines, and demonstrate their validity theoretically and experimentally. Experiments suggest that the new quasi-Monte Carlo method is also more efficient than the conventional approach based on surface tessellation.     

Visibility‐Aware Progressive Farthest Point Sampling on the GPU

In this paper, we present the first algorithm for progressive sampling of 3D surfaces with blue noise characteristics that runs entirely on the GPU. The performance of our algorithm is comparable to state‐of‐the‐art GPU Poisson‐disk sampling methods, while additionally producing ordered sequences of samples where every prefix exhibits good blue noise properties. The basic idea is, to reduce the 3D sampling domain to a set of 2.5D images which we sample in parallel utilizing the rasterization hardware of current GPUs. This allows for simple visibility‐aware sampling that only captures the surface as seen from outside the sampled object, which is especially useful for point‐based level‐of‐detail rendering methods. However, our method can be easily extended for sampling the entire surface without changing the basic algorithm. We provide a statistical analysis of our algorithm and show that it produces good blue noise characteristics for every prefix of the resulting sample sequence and analyze the performance of our method compared to related state‐of‐the‐art sampling methods.     

Wide Baseline Matching between Unsynchronized Video Sequences

3D reconstruction of a dynamic scene from features in two cameras usually requires synchronization and correspondences between the cameras. These may be hard to achieve due to occlusions, different orientation, different scales, etc. In this work we present an algorithm for reconstructing a dynamic scene from sequences acquired by two uncalibrated non-synchronized fixed affine cameras. It is assumed that (possibly) different points are tracked in the two sequences. The only constraint relating the two cameras is that every 3D point tracked in one sequence can be described as a linear combination of some of the 3D points tracked in the other sequence. Such constraint is useful, for example, for articulated objects. We may track some points on an arm in the first sequence, and some other points on the same arm in the second sequence. On the other extreme, this model can be used for generally moving points tracked in both sequences without knowing the correct permutation. In between, this model can cover non-rigid bodies with local rigidity constraints. We present linear algorithms for synchronizing the two sequences and reconstructing the 3D points tracked in both views. Outlier points are automatically detected and discarded. The algorithm can handle both 3D objects and planar objects in a unified framework, therefore avoiding numerical problems existing in other methods. This work was done while the authors were PhD students in the School of Computer Science and Engineering, the Hebrew University of Jerusalem.