As a key of airborne laser data processing, the filtering of point cloud has been an important prerequisite and research focus of data applications.
作为机载激光雷达数据处理的关键环节，激光雷达点云滤波一直是数据应用的重要前提和研究热点。
TIN surface from a point cloud.
从点云对象创建曲面。
Read the LIDAR point cloud data, and can be displayed.
读取LIDAR的点云数据，并且可以显示出来。
Results are shown in the end, which are dense point cloud.
最终结果以稠密点云的形式在文末显示。
A new modeling method for scattered point cloud is presented.
本文提出了一种散乱点云数据的建模新方法。
This complicates the estimation of local point cloud 3D features.
这使得3D点云特征估计变得复杂。
The discrete point cloud data sampling from the striation figure is common.
采样于线状图形的离散点云数据也是常见的。
A method of data preprocessing based on scattered point cloud was proposed.
提出了一种基于散乱点云的数据预处理方法。
Its point cloud could be transformed into mesh model by a series of computer software.
将其点云利用一系列的软件转换成三维网格模型。
A method of extracting 3d building model from airborne lidar point cloud was presented.
提出了针对机载激光雷达点云的建筑物三维模型提取方法。
Then the modified point cloud was optimized with a curve fitting method by moving window.
再通过移动窗口曲面拟合，对结果进一步优化。
Based on precisely matching of point cloud, this paper proposes an improved ICP algorithm.
针对点云精确匹配，提出一种改进的ICP算法。
Using LS5000 3d laser scanner, we can acquire the point cloud data of the real actor's face.
利用LS 5000型三维激光扫描仪扫描真实演员人脸，可以得到其三维人脸点云数据。
Airborne lidar point cloud filtering is the key of acquiring DSM and DEM with high precision.
机载激光点云数据滤波是获取高精度数字表面模型和数字高程模型的关键。
The combined effects of various factors mainly reflected in the accuracy of point cloud data.
而各种因素的综合影响则主要反映在点云数据的精度上。
Fig. 4 Comparison of LiDAR point cloud data and traditional optical image. (a) UAV LiDAR data;
图4激光雷达点云数据和传统光学影像数据比较。 （a）无人机激光雷达点云数据；
The measurement of scattered point cloud data is the foundation of surface reconstruction of a 3d object.
散乱点云数据的测量是三维物体曲面重建的前提和基础。
The results show that this point cloud registration error model and the experimental method are excellent.
结果表明点云误差传播模型正确，试验方法良好。
In this paper, point cloud getting theory, point cloud index method and point cloud filter method are concerned.
本文在点云获取理论、点云索引方法、点云滤波等方面进行了较为深入的研究。
Through point cloud data acquired from the building, the course of registration based ICP algorithm is provided.
通过建筑物三维激光扫描数据的采集，对基于ICP算法的点云数据配准过程进行了详细地分析。
Spatial 3d point cloud can be quickly captured on object surface by laser scanning, however its data volume is huge.
三维激光扫描获得的点云数据，其数据量比较大。
There is noise and defective data on the 3d scanning point cloud. A robust mesh reconstruction algorithm is proposed.
由于3维扫描点云通常存在噪音和缺失数据，提出了一种鲁棒的点云网格重建算法。
The grid index based on linear quadtree is used to index the point cloud data, extracting the point cloud on the block.
采用基于线性四叉树的格网索引对点云数据建立索引，对点云数据进行分块提取；
This paper proposes a geometry image representation based progressive transmission of point cloud surface on the network.
文章提出了基于几何图像表示的点云曲面网络渐进传输方法。
Finally, NURBS surface is fitted by point cloud of the blade and the cylinder surfaces are fitted by point cloud of gauges.
最后，分别拟合叶片点云和量规点云为NURBS曲面和圆柱体曲面。
Through the segmentation of the spatial box bounded by point cloud, efficient extraction of boundary information is implemented.
通过对点云数据进行空间三维划分，实现了边界信息的高效提取。
The high precision and reliability of the reconstruction model are reached by increasing sample density of measuring point cloud.
通过增加点云的采样密度，提高重建模型的精度和可靠性。
The high precision and reliability of the reconstruction model are reached by increasing sample density of measuring point cloud.
通过增加点云的采样密度，提高重建模型的精度和可靠性。
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