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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。
- 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。
- 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。
- 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。
- 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。
- This paper takes the Hough Transform to simulate a velocity profile by using the anti-noise advantage of Hough Transform to eliminate the noise points. 利用霍夫变换抗干扰（或噪声）能力强的优点，可将干扰点（或噪声）剔除，然后用霍夫变换拟合流速剖面。
- 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。
- This method has been validated through different examples, using the autobody point cloud data obtained from the ATOS measurement equipment. 并结合ATOS测量设备得到的车身曲面点云数据，给出不同的实例，证明了该方法的有效性。
- If the point cloud is already polygonized, you must confirm to continue creating a new polygonization (and destroy the existing one). 如果点云已被多边形化，你必须确认继续创建一个新的多边形（破坏存在的多边形）。
- A loud noise from the street diverted my attention. 街上一阵喧闹声转移了我的注意力。
- By default, they're small yellow points. You'll also notice a bounding box around the particles to indicate that the point cloud is selected. 在默认的情况下，发射出来的粒子是黄色的点。同时有一个边界盒子围绕在粒子群周围标志着粒子当前为选择状态。
