A novel algorithm for fitting surface reconstruction of unorganized data points is presented in this paper.
给出了一个新的散乱数据的曲面重建算法。
The study of curve reconstruction based on unorganized data points has great importance in reverse engineering.
在反求工程中，基于散乱数据点的曲线重建研究有着重要的意义。
Here's an example: unorganized data, such as a group of measurements, is usually processed in some manner to make it easily readable for users, such as a graph or a chart.
下面是一个示例：未经组织的数据(如一组测定数据)通常采用某种方式进行处理，以便用户能方便地阅读，如处理为图形或图表。
You'll want to limit this variability, however, as it tends to make data unorganized, and thus not easy to find or manage.
不过您需要限制该变化性，因为它往往使数据变得无序，从而不易于查找或管理。
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。
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。
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。
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