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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。
The algorithm is based on an incrementally expanding Neural Network and the statistical analysis of its learning process for an unorganized point cloud. 首先应用神经网络的方法对点集模型进行学习，根据在学习过程中神经网格法线的变化情况，找到特征点。
unorganized point cloud 三维散乱点云数据
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。
A systematic scheme is proposed to automatically extract geometric surface features from a point cloud composed of a set of unorganized three-dimensional coordinate points by data segmentation. 给出了数据分块系统性方案,即从仅含有三维坐标的散乱的点云中自动提取几何曲面特性。
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。
A rapid incremental surface approximation algorithm is proposed to establish B-spline surface from the unorganized point cloud.The algorithm is composed of four steps. 摘要针对用接触式三维点数据获取设备快速输入的物体表面散乱点云数据，提出了增量式B样条曲面快速逼近算法。
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。
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。
Go all through the unorganized points, and distribute it to a small interspace box according to its three coordinates. 遍历所有散乱数据点，按照其三个方向上的坐标分到其所在的空间格内。
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。
The basic process and several typical algorithms of surface reconstruction from unorganized points are introduced first, then the procedure and research range are defined. 本文首先介绍了散乱点曲面重建的基本过程及各种方法，明确了散乱点曲面重建的步骤及研究范畴；
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。
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。
We use a size changeable adjacent field to describe the topological structure of 3D unorganized points in our algorithm.It can offer essential dynamic information for tessellation and points" normal. 算法采用可以控制大小的邻域作为空间散乱数据点的拓扑关系的几何描述，为网格划分和点的法向量的几何描述提供了必要的动态几何信息。
Strategies for surface reconstruction have proceeded in two main directions:reconstruction from unorganized points and reconstruction that exploits the underlying structure of the acquired data. 对于表面的重建策略已经在二个主要的方向着手进行:来自不组织的点和开发已取得数据的在下面结构的重建的重建。
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。