改进的三维点集凸包求取算法

为提高三维点集凸包的求取效率,提出充分利用凸包极值点和性质改进的三维点集凸包求取算法.首先,求出三维点集中的极值点,并由它们形成初步凸包;其次,根据初步凸包与点的位置关系,排除其内部点;最后,依次考察其外部点,求出符合要求的点集、棱边集和面集,并对凸包进行扩展,得到凸包的点集、棱边集和面集.与普通算法进行时间的复杂度分析比较及实验表明,该算法效率较高.     

一种高效的平面点集凸包递归算法

凸包是计算几何的基本结构, 在许多图形图像相关领域得到了广泛应用. 本文提出了一种简单快速的平面点集凸包算法, 使用了主成分分析法(Principle component analysis, PCA)对点集进行预处理, 并研究了适用的排序规则和凸包边缘点判定原则. 该算法已成功应用于一光栅投影三维形貌快速测量系统,对相位干涉图中密集残留点所形成的最小凸包进行提取. 系统将提取的凸包区域进行掩码标记, 从而避免密集残留点造成相位展开错误, 保证了三维形貌重构的准确性. 实验结果表明, 该算法准确可靠, 并且运行效率较高.     

基于自适应椭圆距离的点云分区精简算法

利用传统点云精简算法进行散乱点云简化会导致点云模型部分细节特征的丢失或模糊以及影响非平面区域的光顺性。针对这些问题,提出基于自适应椭圆距离的点云分区精简算法。首先,通过对邻域点集进行微切平面与局部曲面的拟合,计算出各点的法矢及曲率等;其次,利用所得几何特征信息,提取点云边界特征以及完成点云平面区域与非平面区域的划分;最后,采用改进后的精简算法对不同区域进行简化。实验结果表明,该算法不但能够快速完成符合要求精简率的数据简化,还能保护点云模型的细节特征以及保证模型非平面部分的光顺性。经过软件分析得出,精简后模型与原始模型的距离误差的标准偏差为0.015 mm。     

基于区域正交化分割的平面点集凸包算法EI北大核心CSCD

为解决实际工程应用中具有超大规模的平面点集的凸包计算问题,提出了一种基于点集所在区域正交化分割的新算法.利用点集几何结构的部分极点对平面点集进行正交化分割,以获取不相干的点集子集簇,再对所有点集子集分别计算其凸包极点,最后合并极点得到凸包点集.在不同层级的正交化分割过程中,根据已知极点的信息,逐层舍去对于凸包极点生成没有贡献的无效点,进而提高算法运行效率.在与目前常用凸包算法的对比实验中,该算法处理超大规模的平面点集时稳定性高且速度更快.     

基于连续HLOD的大规模场景快速绘制算法

大规模场景的快速绘制是虚拟现实技术重要的研究课题之一.为了加速场景的绘制,一般采用层次细节模型和可见性裁剪方法,但是现有算法在处理大规模场景时存在着局限性.本文提出了一种新的大规模场景快速绘制算法,该算法在场景层次划分的基础上,利用拓扑结构可变的网格简化方法为场景层次计算连续的分层层次细节模型（HLOD）;然后在实时绘制阶段,对场景分层层次细节模型进行视点相关的全局和局部细化,并结合快速有效的视域裁剪,从而大大加速了场景绘制速度.实验结果表明该算法是简单有效的,并且算法还可以进一步扩展到外存方式.     

大规模外存场景的交互绘制

在场景分割的基础上,提出一种连续分层层次细节模型组织场景,并对视点空间进行划分,为每个视点单元计算恰好满足单元内任意视点屏幕像素误差的场景图节点列表,称为cell-front;然后利用cell-front对外存模型的存储进行重新设计.在绘制时采用多线程技术,绘制线程对当前cell-front进行可见性剔除和视点相关的选择绘制;预取线程采用一种基于视点单元的预取策略,利用视点单元之间cell-front的变化控制预取数据的调度.该算法能够在保证场景绘制质量的前提下,在普通的PC机上实现大规模外存场景的实时交互显示.     

完全拓扑关系结构D-TIN多核并行构建

为了充分利用普通PC机多核CPU计算资源实现大数据量离散点集矢量地形快速构建,提出具有完全拓扑关系结构D-TIN的多核并行生成算法.首先基于四方边缘结构设计具有完全拓扑关系的D-TIN数据结构及系列函数接口,用于操作该数据结构;然后顾及线程间数据负载平衡和空间均质性,采用坐标排序法对离散点集进行均等虚拟划分;再基于离散点集凸包"生成—消融—生成"周期性特征和分治算法思想设计D-TIN串行构建算法;最后以其作为D-TIN并行构建算法的内核,Open MP作为多核并行编程模型,实现了单机多核环境下的D-TIN并行构建.实验结果表明,当数据量较大时,该算法效率明显高于串行算法,且两者的计算结果具有完全一致性;同时,参与计算线程数与总耗时呈负相关.     

基于有序简单多边形的平面点集凸包快速求取算法

凸包问题是计算几何的基本问题之一，在许多领域均有应用。传统平面点集凸包算法和简单多边形凸包算法平行发展，互不相干。本文将改进的简单多边形凸包算法应用于平面点集凸包问题中，提出了新的点集凸包算法。该算法首先淘汰掉明显不位于凸包上的点，然后对剩余点集排序，再将点集按照一定顺序串联成有序简单多边形，最后利用前瞻回溯方法搜索多边形凸包，从而得到点集的凸包。本文算法不仅达到了Ｏ的理论时间复杂度下限，而且算法     

层次可见性与层次细节地表模型相结合的快速绘制

大规模地表模型的实时绘制是虚拟现实技术中重要的研究课题之一。为了加速地表模型的绘制，人们采用视点相关的动态多分辨率层次细节模型方法，但是算法效率依然有待提高。该文提出一种层次可见性与层次细节地表模型相结合的快速地形绘制方法。算法旨在利用地表模型所具有的horizon特性在预处理中为地表多分辨率块模型建立相应的层次“块”可见性结构，快速判定地形块相对于当前视点的可见性，以减少多分辨率模型中模型细节的处理和绘制三角形的数目。同时为消除地表模型层次变化所带来的可见性错误，算法提出一种层次结构可见性计算方法，以修正多分辨率模型所带来的可见性动态变化。实验结果表明算法有效地提高了绘制效率，是可行的。     

基于特征点检测的最佳视点选择方法

针对三维模型视点选择问题,提出了基于特征点检测的最佳视点选择方法。算法首先根据三维网格模型的顶点邻接区域信息计算顶点的局部高度,即顶点的显著度大小;然后基于顶点的局部高度值大小,结合非极大值抑制算法进行三维模型表面特征点检测;最后针对视点球体上的每个候选视点分析该视点下可见特征点的几何分布信息,计算视点质量,从而筛选出最佳视点。实验结果验证了基于特征点检测的三维模型视点选择方法的合理性和优越性,所选择的最佳视点能够提供三维模型较多的几何结构和视觉特征信息。     

基于点的绘制与压缩技术

基于点的模型数据往往非常庞大,因此怎样在有限的硬件环境下对庞大的点云数据进行压缩传输和实时绘制成为基于点的图形学中两个最重要的问题.回顾了基于点的绘制的最新研究进展,将基于点模型的压缩算法分为渐进压缩编码和单分辨率压缩编码两类分别进行介绍和分析比较,并给出了一些研究建议.     

面向三维模型分割的边界感知点云神经网络

为了能够更好地应用深度神经网络学习三维模型的空间特征,获得更好的三维模型分割效果,提出面向三维模型分割的边界感知点云神经网络.首先,采用边界感知的网格点云化方法,将网格分割问题转化成点云标记问题;然后,利用数据切片方法对转化而来的点云数据进行重采样;最后,利用不同大小卷积核的滤波器提取点云数据的空间特征,并将点云标记的结果对应到原网格模型,得到三维模型分割的结果.在ShapeNetCore数据库上的实验结果表明,该方法不仅能够明显地提高分割的准确率,而且具有边界感知的特性,能够有效地避免过分割现象.     

Reconciling point-based and interval-based semantics in temporal relational databases: a treatment of the Telic/Atelic distinction

The analysis of the semantics of temporal data and queries plays a central role in the area of temporal databases. Although many different algebrae and models have been proposed, almost all of them are based on a point-based (snapshot) semantics for data. On the other hand, in the areas of linguistics, philosophy, and recently, artificial intelligence, an oft-debated issue concerns the use of an interval-based versus a point-based semantics. In this paper, we first show some problems inherent in the adoption of a point-based semantics for data, then argue that these problems arise because there is no distinction drawn in the data between telic and atelic facts. We then introduce a three-sorted temporal model and algebra including coercion functions for transforming relations of one sort into relations of the other at query time which properly copes with these issues.     

ASM: An adaptive simplification method for 3D point-based models

Due to the popularity of computer games and computer-animated movies, 3D models are fast becoming an important element in multimedia applications. In addition to the conventional polygonal representation for these models, the direct adoption of the original scanned 3D point set for model representation is recently gaining more and more attention due to the possibility of bypassing the time consuming mesh construction stage, and various approaches have been proposed for directly processing point-based models. In particular, the design of a simplification approach which can be directly applied to 3D point-based models to reduce their size is important for applications such as 3D model transmission and archival. Given a point-based 3D model which is defined by a point set P () and a desired reduced number of output samples n^s, the simplification approach finds a point set P_s which (i) satisfies |P_s|=n^s (|P_s| being the cardinality of P_s) and (ii) minimizes the difference of the corresponding surface S_s (defined by P_s) and the original surface S (defined by P). Although a number of previous approaches has been proposed for simplification, most of them (i) do not focus on point-based 3D models, (ii) do not consider efficiency, quality and generality together and (iii) do not consider the distribution of the output samples. In this paper, we propose an Adaptive Simplification Method (ASM) which is an efficient technique for simplifying point-based complex 3D models. Specifically, the ASM consists of three parts: a hierarchical cluster tree structure, the specification of simplification criteria and an optimization process. The ASM achieves a low computation time by clustering the points locally based on the preservation of geometric characteristics. We analyze the performance of the ASM and show that it outperforms most of the current state-of-the-art methods in terms of efficiency, quality and generality.     

Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events

Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns.     

一种点面混合的复杂三维模型加速绘制方法

在主流个人计算机硬件条件下，为加速百万以上三角面片构成的复杂稠密几何模型的绘制速度，综合基于几何的建模与绘制方法GBMR和基于点的建模与绘制方法PBMR的优点，提出了一种同时使用三角面片和点作为基本单元进行对象建模与绘制的点面混合方法．在预处理阶段，对模型表面进行网格分割，存储子块三角面片和顶点点云数据，同时对顶点点云按顶点重要度排序并序列化为线性结构．在实时绘制阶段，进行视相关的裁剪和背面剔除，不同子块按视点距离分别由三角形或点进行绘制．以上过程充分利用图形处理单元GPU，实现了基于GPU的点面混合的对象连续多分辨率绘制，有效地提高了复杂模型的绘制效率．     

基于数据类型转换的点云快速有损压缩算法

针对海量三维点云数据为计算机存储和传输增加沉重负担的问题,提出一种基于数 据类型转换的点云快速有损压缩算法。首先设计出一种数据类型转化规则-FtoI 规则,根据FtoI 规则将浮点数类型点云转换成整数类型点云,然后将整数类型点云切分成许多小单元面块,每一 单元点云生成最小生成树,按广度优先的顺序对树形结构进行编码。同时,按照树形结构对父子 节点的差值进行编码,把整型差值分成两部分编码,符号一部分,其绝对值一部分,其中绝对值 部分采用算术编码进行压缩。实验表明该文算法在保证整个三维点云模型的质量情况下,具有不 错的压缩速度和压缩率。     

Scalable skyline computation using a balanced pivot selection technique

Skyline queries have recently received considerable attention as an alternative decision-making operator in the database community. The conventional skyline algorithms have primarily focused on optimizing the dominance of points in order to remove non-skyline points as efficiently as possible, but have neglected to take into account the incomparability of points in order to bypass unnecessary comparisons. To design a scalable skyline algorithm, we first analyze a cost model that copes with both dominance and incomparability, and develop a novel technique to select a cost-optimal point, called a pivot point, that minimizes the number of comparisons in point-based space partitioning. We then implement the proposed pivot point selection technique in the existing sorting- and partitioning-based algorithms. For point insertions/deletions, we also discuss how to maintain the current skyline using a skytree, derived from recursive point-based space partitioning. Furthermore, we design an efficient greedy algorithm for the k representative skyline using the skytree. Experimental results demonstrate that the proposed algorithms are significantly faster than the state-of-the-art algorithms.     

Efficient image reconstruction for point-based and line-based rendering

We address the problem of an efficient image-space reconstruction of adaptively sampled scenes in the context of point-based and line-based graphics. The image-space reconstruction offers an advantageous time complexity compared to surface splatting techniques and, in fact, our improved GPU implementation performs significantly better than splatting implementations for large point-based models. We discuss the integration of elliptical Gaussian weights for enhanced image quality and generalize the image-space reconstruction to line segments. Furthermore, we present solutions for the efficient combination of points, lines, and polygons in a single image.     

非规则三维碎片部分匹配算法研究

针对三维碎片拼合中的碎片匹配问题,在提取碎片轮廓线的基础上,提出了一种快速的碎片匹配算法。该算法首先对轮廓特征点按其邻域曲面片的形状进行分类;其次根据特征点类型标志、曲率及特征段Hausdorff距离对不同轮廓线上的特征段进行相似性度量;最后利用法矢对相似度较高的轮廓段进行可匹配性验证。该算法较适合于用散乱点云表示的三维碎片模型。