空间二仿射对应点列形成的双曲抛物面

空间两条直线交叉,且该两条直线是二仿射对应点列的底,其对应点的连线包络成的二次曲面称为双曲抛物面.借助于正投影图,讨论了双曲抛物面图示法的特点和截交线的变化规律.     

空间两射影对应点列形成的单叶双曲面

空间两条交叉直线为底的射影对应点列,对应点连线包络成单叶双曲面。 如果移动其中任一点列的位置,单叶双曲面的形状变化有一定的规律。通过对单叶双曲面截 交线形状的系统分析,首次提出单叶双曲面上存在抛物线型截交线。针对二射影对应点列作 图法的简化,为我们进一步开发应用CAD 软件自动生成该曲面提供了理论支持。     

基于OpenGL的3D模型浏览、测量及管理

通过解析3DS格式的模型数据结构，借助于OpenGL实现了3D模型的浏览。通过高精度的交互操作、实现模型的点拾取功能是该文的创新点，在此基础上可以对模型进行测量及数据管理。该文介绍的技术极大地提高了3D模型的表现能力及应用价值。     

基于Java3D的不规则形体三维造型及真实感处理

针对Java3D只具有规则形体的三维造型类，无法完成不规则形体的三维造型这一问题，提出用小面片来逼近曲面实现回转体的造型，提出一种变截面曲柄的三维造型方法，同时实现了渲染和真实感处理，是对Java3D功能的必要扩充。     

基于X3D的三维虚拟图书馆的构建

该文阐述了利用X3D技术对图书馆内场景进行建模的方法,研究了三维场景的交互和优化,并结合多媒体技术实现了基于Web的三维虚拟图书馆。     

基于Multipath的煤矿巷道三维模型构建方法

针对现有煤矿巷道三维模型存在灵活性和可移植性差、数据更新维护困难等问题,提出了一种基于Multipath的煤矿巷道三维模型构建方法。该方法通过定义点弧拓扑数据模型,将煤矿CAD格式数据转换为Geodatabase中的要素类,利用开发的ArcGIS Engine程序、通过插入节点坐标生成巷道三维模型,实现了煤矿巷道自动建模,有效减少了巷道三维建模的工作量。     

基于栅格的三维GIS缓冲体分析研究

缓冲区分析是二维GIS空间分析的基本功能,但目前对三维GIS中的缓冲体分析研究较少。该文对三维缓冲体分析的意义进行了分析,并针对地质应用,根据生长元是否向周围均匀扩散及扩散周围是否存在障碍物,对三维缓冲体的种类进行了划分。提出了有约束缓冲体的生成算法,讨论了非均质的缓冲体的生成算法。     

基于深度学习的三维目标检测算法综述

随着自动驾驶行业的快速发展,基于深度学习的三维目标检测技术也得到了快速发展,目前自动驾驶汽车主要依赖图像与激光雷达点云进行环境感知。基于这两种数据的三维目标检测技术可提取出物体的空间结构信息,包括物体的姿态、尺寸、运动方向、形状等,因此该技术不仅可用于自动驾驶的感知,还可用于工业机器人对物体的识别与抓取,以及仓储机器人的视觉导航等。近年来,计算能力的提升、数据集的公开、深度学习的发展,为三维目标检测算法带来了巨大的变革。     

基于OpenMP的两个地学基础空间分析算法的并行实现及优化

研究了两个基础空间分析算法(大量线段求交、点面叠加)的实现方法,并基于单机多核环境,利用()pcnMP实现了并行算法。通过分析并行算法,得到了无法取得线性加速比的原因,即负载不均衡,内存管理采用全局方法,仍然是“串行”的。基于此,通过对数据进行有效的排序并利用()pcnMP的动态调度方式进行调度;改进了现有的并发内存分配技术,并将其用于并行算法的内存管理。利用以上方法对并行算法进行了优化,测试表明,优化后的算法具有较为理想的近线性加速比,单机四核环境下,每个核心的计算效率不低于80%。     

基于混沌映射的散列函数加密算法分析及设计

混沌序列具备散列函数所要求的单向性、扩散性、初值敏感性等非常多的特性,在混沌映射的基础上可以设计出优秀的散列函数来进行加密算法设计。密码学和混沌两者有着类似的结构和紧密的关系,这也使得混沌密码算法成为当代信息安全的重要研究内容。作为一种新的构造散列函数的方法,混沌映射正得到越来越多的关注。本文主要研究一种基于广义混沌映射的散列函数的加密算法,通过更改混沌方程的控制参数和状态值,更改混沌映射的方程获得不同的散列结果,可以更有效的防止攻击。     

Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis

3D face scans have been widely used for face modeling and analysis. Due to the fact that face scans provide variable point clouds across frames, they may not capture complete facial data or miss point-to-point correspondences across various facial scans, thus causing difficulties to use such data for analysis. This paper presents an efficient approach to representing facial shapes from face scans through the reconstruction of face models based on regional information and a generic model. A new approach for 3D feature detection and a hybrid approach using two vertex mapping algorithms, displacement mapping and point-to-surface mapping, and a regional blending algorithm are proposed to reconstruct the facial surface detail. The resulting models can represent individual facial shapes consistently and adaptively, establishing facial point correspondences across individual models. The accuracy of the generated models is evaluated quantitatively. The applicability of the models is validated through the application of 3D facial expression recognition using the static 3DFE and dynamic 4DFE databases. A comparison with the state of the art has also been reported.     

3D hyperbolic Voronoi diagrams

Voronoi diagrams have useful applications in various fields and are one of the most fundamental concepts in computational geometry. Although Voronoi diagrams in the plane have been studied extensively, using different notions of sites and metrics, little is known for other geometric spaces. In this paper, we are interested in the Voronoi diagram of a set of sites in the 3D hyperbolic upper half-space. We first present some introductory results in 3D hyperbolic upper half-space and then give an incremental algorithm to construct Voronoi diagram. Finally, we consider five models of 3D hyperbolic manifolds that are equivalent under isometries. By these isometries we can transform the Voronoi diagram of each model to others.     

实时交互的带浮雕纹理的三维模型构建方法

为了快速生成带浮雕纹理的三维模型,提出一种实时交互的浮雕纹理模型构建方法。方法分两步：第一步,将生成浮雕的源模型或图像转换为初始深度图,并进一步转换为梯度图,再通过梯度域的压缩、过滤,求解线性方程重建出整体连续的浮雕深度图;第二步,借助基于网格求交的浮雕纹理映射算法将浮雕深度图贴在目标模型表面,并通过移动、旋转、缩放等操作实时在目标模型三维空间上修改浮雕效果,最终重建目标模型网格,生成浮雕纹理模型。实验表明,所提方法可快速实现在一个目标模型上生成凹浮雕、凸浮雕、多浮雕等效果,所得模型无需经过其他处理,可直接应用于3D打印,打印效果较好。     

海量3D点云数据压缩与空间索引技术

针对3D模型中海量点云数据压缩与空间索引低效问题和漫游过程中相邻两次查询窗口重叠是大概率事件问题,提出邻点差值渐进压缩和基于裁剪重叠区域进行冗余处理的R树空间索引方法。首先,利用八叉树对3D模型进行空间剖分,借助Morton码对每个叶节点管理的点云数据排序,按照R树叶节点的外接立方体大小对数据进行分块,计算块内相邻点数据差值,以块为单位渐进压缩差值,批量读取这些数据块创建R树;其次,借助上次查询窗口范围计算本次查询有效范围;最后,给出基于R树索引的点云数据查询方法。该方法使点云数据压缩率提高了26.61个百分点,并能实现流式传输,同时减少了I/O开销,使其查询性能提高了35.44%,数据冗余减少了16.49个百分点。实验结果表明,所提方法在3D虚拟旅游、数字城市等系统具中有明显优势。     

A method of 3D modeling and codec

3D modeling and codec of real objects are hot issues in the field of virtual reality. In this paper, we propose an automatic registration two range images method and a cycle based automatic global registration algorithm for rapidly and automatically registering all range images and constructing a realistic 3D model. Besides, to meet the requirement of huge data transmission over Internet, we present a 3D mesh encoding/decoding method for encoding geometry, topology and attribute data with high compression ratio and supporting progressive transmission. The research results have already been applied successfully in digital museum. Supported by the National Natural Science Foundation of China (Grant Nos. 60533070, 60773153), the Key Grant Project of Chinese Ministry of Education (Grant No. 308004), the Project of Chinese Ministry of Science and Technology (Grant No. 2006BAK12B09), and the Project of Beijing Municipal Science and Technology Commission (Grant No. Z07000100560714)     

Stabilizing 3D modeling with geometric constraints propagation

This paper proposes a technique for estimating piecewise planar models of objects from their images and geometric constraints. First, assuming a bounded noise in the localization of 2D points, the position of the 3D point is estimated as a polyhedron containing all the possible solutions of the triangulation. Then, given the topological structure of the 3D points cloud, geometric relationships among facets, such as coplanarity, parallelism, orthogonality, and angle equality, are automatically detected. A subset of them that is sufficient to stabilize the 3D model estimation is selected with a flow-network based algorithm. Finally, a feasible instance of the 3D model, i.e. one that satisfies the geometric constraints and whose 3D vertices lie within the associated polyhedral bounds, is computed by solving a Constraint Satisfaction Problem. The process accommodates uncertainty in a non-probabilistic fashion and thus provides rigorous results. Synthetic and real experiments illustrate the approach.     

浅析3D动漫造型协同创新设计方法

动漫造型的设计是一项依赖于脑力、创造力以及想象力的活动,能够体现出知识经济的特点,其中需要交互式的图形系统和分析计算为主的计算机辅助设计系统,这样才能为设计人员提供工具,实现人机交互．但是这并不能为设计人员的创造性思维有所帮助,也不能支持协同设计。本文首先对3D动漫造型的发展情况进行简述,之后分析3D动漫造型协同创新设计研究现状,结合现状提出3D动漫造型协同创新设计方法,从而引发动漫产业在国际市场上的发展,保证期发展前景的广阔性。     

采用草绘轮廓的3维人脸建模方法

为方便用户进行3维人脸形状设计,提出一种基于手绘轮廓的3维人脸建模方法。该方法的主要特点在于,一方面,引用姿态估计技术对人脸草图进行解析,将用户绘制的侧视人脸草图转换成对应的正视人脸草图,可支持用户选择多个视角绘制人脸;另一方面,采用多层映射机制建立人脸草图特征点与3维人脸特征点之间的一一对应关系,由对应特征点之间的形变量来控制生成3维人脸,保证草图笔画的几何形状信息能有效映射到3维模型中。实验结果表明,文中方法能快速生成形状新颖的特定人脸,可有效支持用户进行3维人脸形状的手绘建模。     

DSACNN: Dynamically local self-attention CNN for 3D point cloud analysis

The point cloud is a common 3D representation widely applied in CAX engineering due to its simple data representation and rich semantic information. However, discrete and unordered 3D data structures make it difficult for point clouds to understand semantic information and make them unsuitable for applying standard operators. In this paper, to enhance machine perception of 3D semantic information, we propose a novel approach that can not only directly process point cloud data by a novel convolution-like operator but also dynamically pay attention to local semantic information. First, we design a novel dynamic local self-attention mechanism that can dynamically and flexibly focus on top-level information of the receptive field to learn and understand subtle features. Second, we propose a dynamic self-attention learning block, which adopts the proposed dynamic local self-attention learning convolution operation to directly deal with disordered and irregular point clouds to learn global and local point features while dynamically learning the important local semantic information. Third, the proposed operation can be compatibly applied as an independent component in popular architectures to improve the perception of local semantic information. Numerous experiments demonstrate the advantage of our method for point cloud tasks on datasets from both CAD data and scan data of complex real-world scenes.