【摘要】 外存模型是指其规模远远超出内存容量的海量模型 .为提高其存储、传输、显示等操作的效率 ,对外存模型进行渐进式的压缩是非常重要的 .但当前已有的外存模型压缩算法都是单一层次的 ,不能做到渐进压缩 .为此 ,该文提出一种针对外存模型的渐进压缩方法 ,能高效地压缩外存模型 ,并进行多分辨率的传输和显示 .该方法首先将外存模型的包围盒空间按照八叉树形式进行剖分和层次化组织 ,使得最精细层次的各个立方块空间中的局部模型都能完全装入内存进行处理 ;然后 ,在各个立方块中对局部的模型进行基于MarchingCubes方式的重新拟合 ,并在此基础上建立各个局部的自适应八叉树 ;最后 ,基于各个局部自适应的八叉树 ,由粗至细渐进地遍历全局自适应八叉树的各个节点 ,并利用对内存模型能高效渐进压缩编码的先进方法进行编码压缩 .实验表明 ,该方法对外存模型的压缩比达到了与处理内存模型相似的压缩比 ,高于目前的外存模型压缩方法 ,是第一个能渐进压缩外存模型的方法 .更多还原

【Abstract】 Out-of-Core models are the massive models that cannot be loaded into the memory as a whole. For improving the efficiency of storing, transmitting and rendering such models, it is very important to progressively compress the models. However, to our knowledge, all the existing out-of-core compression algorithms are single rate, which cannot perform progressive compression. With regard to this, this paper proposes a method that can progressively compress out-of-core models in high efficiency and transmit and render the models in multi-resolutions. At first, the method uniformly divides the bounding box of the out-of-core model into sub-boxes for the local model in every sub-box to be able to process in core, and manages the sub-boxes hierarchically in an octree. Afterwards, the local model in every sub-box is reconstructed with Marching Cubes and an adaptable sub-octree is constructed for the reconstructed local model. Finally, based on the sub-octrees, the nodes of the octree of the whole model can be traversed progressively from coarse to fine and compressed with an advanced compression method for handling in-core models. Experimental results show that the new method can compress the out-of-core models in similar compression ratios as the advanced method to compress the in-core models,it is also superior to the existing methods for compressing out-of-core models and is the first method for performing progressive compression on out-of-core models.更多还原