一遍完成的平衡布点外存模型简化算法

Balanced Tilling Based Out-of-Core Simplification

现存的自适应采样的外存模型简化算法均需要多次读取原模型 ,算法效率较低 .该文给出一种仅仅需要读取原模型一遍的自适应顶点聚类算法——平衡布点算法 (Balanced Tilling,BT) ,用于外存模型简化 .其关键思想在于通过表面编码记录模型表面 ,通过对原模型的二次量化 (quadric quantization)得到原模型上的细节分布 .该算法可以定位出所有类型的细节区域 ,而其它一些算法只能定位细节边 .细节区域将被进一步细化 ,而平滑区域将被进一步简化 .该算法大大减少了输入输出时间 ,尤其适合处理超大规模模型 .内存需求很小 ,只与输出模型规模有关 .

All the existing adaptable out-of-core simplification algorithms need to scan the original model more than one time. So the algorithm efficiency is relatively lower comparing with uniform sampling approaches. This paper presents an adaptive clustering method, called Balanced Tiling (BT), for out-of-core simplification, which only needs one pass over the input model. The main idea behind BT is that the model surface can be recorded using surface coding and the global distribution of surface details can be obtained through quadric quantizing of the original model. The algorithm presented in this paper can position all types of detail areas, while some other out-of-core simplification approaches can only position feature edges. The detail areas will be restored while smooth areas will be further simplified. BT especially suits handling super large models because the I/O time is saved greatly. The memory requirement is small, which is only related with the size of the output model.