【摘要】 现有的快速外存模型简化方法一般是对模型进行近似均匀采样 ,无法根据细节分布对模型进行不同程度的简化 ,从而对于细节分布非均匀的模型失真较大 .给出一种快速的外存模型简化方法 ,用于对无法一次装入内存的大型几何模型进行简化 ,大大改善了细节分布非均匀的模型的简化结果 .该方法首先均匀采样 ,对外存模型快速生成初始简化模型 ,生成的同时计算原模型特征的统计信息 ,然后依此对初始简化模型进行细节迁移和局部精细采样 .该方法在保持近似线性处理速度的同时 ,大大提高了简化模型的质量 ,所需的内存空间也非常小更多还原

【Abstract】 Most existing fast out of core simplification methods are based only on uniform or approximated uniform sampling and thus hard to perform adaptive sampling according to the detail distribution of the models. The loss of visual fidelity may be large when processing models with non uniform distribution of detail. In this paper, an efficient high quality out of core simplification method is presented. It can deal with models that are too large to be loaded into main memory. This method greatly improves the simplification result of the models whose detail is non uniform distributed. In this paper, an initial simplified model is first generated by uniform sampling. A statistical analysis of the original model is also performed at the same time, based on which detail shifting and locally refined sampling to the model can be performed. The algorithm greatly improves the quality of the result model while the processing time is still linear to the model size and the memory cost is also small.更多还原