Traditionally 3d models have been made from data describing information on a regular grid, like the grid of pixels on a monitor.
传统的3d模型像显示器中的像素网格一样，是在规则的网格中由描述信息的数据所构成。
Its regular grid pattern is clearly visible at image upper left; concentric rings of major roadways around the city have been added as the metropolitan area has expanded.
而其独特的井字状城市格局也清晰地呈现在图片的左上角，围绕着城市的环形高速路也正在向周边快速辐射开去。
Then the method of 3-d terrain modeling based on regular grid is studied.
然后研究了基于规则格网的三维地形建模方法。
Firstly, points are indexed by the regular grid, and it is shown how to calculate grid size.
首先，采用规则格网索引地面采样点，并推导了计算格网尺寸的经验公式。
Using regular grid for discretization results in diffraction noise for dip interface, and infilling grid may increase the computing efforts.
采用规则网格进行离散，对于倾斜界面无法避免绕射噪声，若加密网格又过度增加工作量。
At present, holes generating in topology optimization is mostly irregular geometric. However fuselage keel structure is generally regular grid.
目前，连续体拓扑优化形成的孔洞多为不规则形状，而飞机机身龙骨结构多为规则栅格状。
For the sake of realizing some special functions in spatial analysis of GIS, it is necessary to covert irregular vector polygon into regular grid.
在实际的GIS空间分析过程中，为了更加简便快捷的实现某些特定的空间分析功能，常常需要将不规则的矢量多边形区域转化为规则的格网区域。
It projects input space on prototypes of a low - dimensional regular grid that can be effectively utilized to visualize and explore properties of data.
它将高维输入空间的数据映射到一个低维、规则的栅格上，从而可以利用可视化技术探测数据的固有特性。
This paper analyses a regular grid network for two kinds of routing standard theoretically, and derives recursive formular to find the network performance.
本文对一种规则的栅格状网络进行了理论分析，并分别针对两种路由准则，采用递归方法推导出了评价网络性能的计算公式。
In the first model they observe the co-occurrence of keywords with image regions which are created using a regular grid. And they annotate the images by the association probability.
共现模型将图像划分成规则区域，根据图像区域和关键词的共现概率来标注图像，即观察关键词与图像区域的联合发生概率。
In the first model they observe the co-occurrence of keywords with image regions which are created using a regular grid. And they annotate the images by the association probability.
共现模型将图像划分成规则区域，根据图像区域和关键词的共现概率来标注图像，即观察关键词与图像区域的联合发生概率。
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