该文提出了一种用线性逼近方法、基于灰度信息从图像获取物体深度信息，从而实现物体三维表面重建的算法。
This paper presents an algorithm of acquiring depth map using linear approximation based on gray level information. Using the algorithm, surface reconstruction can be easily realized.
基于RBF神经网络的图像重建方法的实质就是用RBF神经网络建立电容测量值到图像灰度值的映射关系模型。
The image reconstruction algorithm based on RBF neural networks uses RBF networks to build the mapping relationship between the capacitance value and the image gray-scale value.
研究证明从灰度位图图像到黑白位图图像、矢量图像，最后实现三维重建的方法在反求工程上的可行性和实用性。
The study proves that the method from gray picture to black and white picture, vectorgraph, and at last rebuilding the 3D mold is feasible and practicable in the reverse engineering field.
本文提出一种新方法——灰度差法进行三维重建。
This paper gives a new method-Grey scale difference methods to do 3d reconstruction.
通过比较分析几种常用的插值算法，采用基于灰度的线性插值方法对断层图像进行插值，有效地提高了三维重建的质量。
After comparing and analyzing several commonly used interpolation algorithms, we use the linear gray-based slice-image interpolation method to improve the quantity of 3d visualization.
通过比较分析几种常用的插值算法，采用基于灰度的线性插值方法对断层图像进行插值，有效地提高了三维重建的质量。
After comparing and analyzing several commonly used interpolation algorithms, we use the linear gray-based slice-image interpolation method to improve the quantity of 3d visualization.
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