一种基于线性八叉树结构的自适应步进立方体算法

在步进立方体（MC）算法的基础上提出了一种基于线性八叉树结构的自适应步进立方体算法LOT－AMC，用于提取数据场中的等值面。该算法利用自适应思想能够大量减少三角面的生成，算法速度也有所提高。同时，本算法利用线性八叉树结构对数据场进行分解和组合，解决了先前AMC算法中的特征遗失问题。

An Improved Adaptive Marching Cube Method

Existing Adaptive Marching Cube (AMC) methods all suffer from a serious defect: the loss of features. We succeeded in removing this defect through a novel use of the wellknown octree: decomposition and combination of data fields. Existing AMC methods all begin with simply divided initiai cubes. We use octree to get very small cubes which retain allthe features. We combine these small cubes into larger ones provided that no features are lost. We repeat the combination process until larger initial cubes can not be formed without losing features. The totol number of initial cubes used by our improved AMC method is about the same as that used by existing AMC methods ; thus efficiency is retained while no features are lost.