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Adaptive slicing to point cloud surface for RPM based on Bar 基于自由曲面点云的RPM自适应分层研究
Multi-layered Geometry Image Representation of Point Cloud Surfaces 点云曲面的多层次几何图像表示
Point cloud surface 点云曲面
Compounded geometry image representation of point cloud surfaces and its application 点云曲面的复合几何图像表示及其应用
Direct fitting surface with point cloud data obtained from autobody scanning is difficult. 点云数据三角化处理是逆向工程及快速原型领域中不可缺少的环节。
Geomagic Qualify processes point cloud data from a 3D scanner. Geomagic Qualify可以处理从3D扫描仪里得到的点云数据。
In order to generate a surface model from point cloud data in reverse engineering, it's very important for reconstructing surface to process point cloud. 摘要逆向工程中点云数据处理是重构表面模型的一个很重要的步骤。
Point cloud surfaces 点云曲面
The point cloud exists, but no particles are emitted on the first frame. 虽然点云已经存在，但是在第一帧并没有粒子发射出来。
Objective To generate an optimal surface model from external nasal point cloud data for constructing the visible nose and restoring the nasal defect patients by CAD/CAM techniques. 目的构建解剖特征清晰的三维外鼻形态表面模型,为建造鼻缺损修复计算机辅助设计/计算机辅助制作系统(computer aided design/computer aided manufacture,CAD/cam)用于可视化数字鼻型库奠定基础。
You can have any number of point clouds in a scene. 当发射粒子时，在场景中可以有多个点云存在。
A region-growing algorithm was proposed to reconstruct triangular meshes from unorganized point cloud. 摘要提出一种对无规则点云进行三角网格重构的区域增长算法。
This shader defines each particle within the volume so that the point cloud doesn't look like a single volumetric mass. 这个阴影组的功能是在某个体积内确定每个粒子以便使点云渲染的时候不至于象一个单独的体积块。
LIDAR data are new data source,and they generate a high spatial resolution "point cloud". 激光雷达(LIDAR)数据是一种新型数据源,它产生的是高密度点云数据。
The Particle Volume Cloud shader renders the point cloud's bounding box as a volume. 粒子体积云材质节点是把点云的边界框内的区域作为一个体积来进行渲染。
This method can triangulate the point cloud, reduce it and smooth it.Finally can get the high-quality fitting curve. 该方法能有效地对点云数据进行三角剖分、精简、平滑去噪处理等操作，并能最终得到满足要求的拟合曲线。
It is necessary to triangulate the point cloud in reverse engineering and rapid prototyping. 点云数据三角化处理是逆向工程及快速原型领域中不可缺少的环节。
This automatically creates a point cloud and sets up the node and compound connections in the ICE Tree that are needed to emit particles. 这样的操作将自动建立一个点云，并同时在ICE树中创建一套连接好的节点组，这些是发射粒子最基本的节点组。
These goal positions are determined via a generalized shape matching of an undeformed rest state with the current deformed state of the point cloud. 这些目标位置通过一个通用的无变形的静止状态和点云的当前变形状态之间的形状匹配来决定。
The point cloud is displayed in UGII as a UDO object based on the development tool of UGII and the pure virtual function of the C++. 摘要基于UGII系统的二次开发工具及C++的纯虚函数，实现了作为UDO对象的海量数据点在UGII中的显示技术。