1.
Algorithm of Scattered Point Cloud Data Reduction Based on Non-uniform Subdivision
基于非均匀细分的散乱点云数据精简算法
2.
Study on Data Registration and Reduction for 3D Point Clouds
三维点云数据拼接与精简技术的研究
3.
A Study of Multi-view Point Cloud Merging and Data Reduction
多视点云数据的拼合与精简技术研究
4.
Curvature Estimation of Scattered-point Cloud Data Based on Bounding Box Method
基于包围盒法的散乱点云数据的曲率精简
5.
Research on data simplification for point cloud in surface reconstruction
用于点云曲面重构的数据精简方法研究
6.
Reduction and Surface Reconstruction of Point Cloud Data of Automotive Seat Based on B-spline
基于B样条的汽车座椅点云数据的精简及曲面重构
7.
Research and Implement on the Algorithm of Point Cloud Denoising and Simplification
点云数据的光顺去噪与简化技术的研究与实现
8.
A Fine Registration Method for 3D Point Clouds in Reverse Engineering
逆向工程中三维点云数据精确拼接方法
9.
Accuracy Check and Analysis of LIDAR Elevation Data
机载LIDAR点云高程数据精度检核及误差来源分析
10.
An Approach on Building Reconstruction from Images,Data Clouds and Vector Maps
利用航空影像、点云数据和矢量图进行简单房屋三维重建方法研究
11.
variable-precision coding compaction
可变精度编码的数据精简法
12.
A error limitation of angle and chordal highness method is used to filtrate clouding point.
对大量数据的精减，利用角度和弦高的最大允许偏差法进行点云精减。
13.
Surface and Parameterization of Automobile Seat Point Data
汽车座椅点云数据的曲面化和参数化
14.
Reduction Algorithm for Scattered Points Based on Model Surface Analysis
基于型面特征的三维散乱点云精简算法
15.
The method was accurate, simple and rapid.
方法可靠,数据准确,精密、快速、简便、经济。
16.
Study on Data Registration and Reduction of Reverse Engineering;
逆向工程中数据拼接与精简技术研究
17.
The advantages of the spline collocation method are less input data, simpler calculation, higher precision and more universal application.
研究表明，样条配点法具有输入数据少、计算简便、精度高和计算程序通用的优点。
18.
Image-Based Edge Automatic Extraction of Point Data
基于图像法的点云数据边界自动提取