非均匀抽样网格简化

提出了一种考虑视点空间中某些重要视点的非均匀抽样网格简化的新方法，是在借鉴了Garland-Heckbert方法的基础上提出的，是一种考虑外观相似性的简化算法。给出并证明了两个判定边界的定理，为抽样提供了理论依据。在简化过程中，该算法通过采用视点空间中某些重要视点对模型进行抽样，使抽中的顶点对（轮廓附近的顶点对）得到适当保护。该算法除具有Garland-Heckbert方法的长处外，还可以在三角面片数较少的情况下（50多个三角面片），尽可能保持模型的重要外观特征，给出了计算0－1图像的外观相似性误差的公式，通过该公式对简化结果进行比较，证明提出的简化算法对保持模型的外观特征是行之有效的。最后对该算法的时间和空间复杂性进行了分析。

MESHES SIMPLIFICATION OF NON-UNIFORM SAMPLING

A new method of mesh simplification of non uniform sampling is presented, which considers some important viewpoints in the viewpoint space, and which uses the quadric error metrics simplification algorithm of Garland and Heckbert. In this simplification method, similarity of appearance is considered. Two theorems that discriminate boundary of a model are proposed so that sampling could be based on them in theory. In the simplification process, the model would be sampled by considering some important viewpoints of the viewpoint space, so that pairs of vertices near contours would be sampled and be preserved. The algorithm performs at extremely low levels of simplification, and preserves similarity of appearance whenever possible. This method is tested through experiments. Experimental results show that this method is effective. Finally, the time and space complexity of this algorithm is analyzed.