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A “Group Marching Cube” (GMC) Algorithm for Speeding up the Marching Cube Algorithm

机译:一种用于加速行进多维数据集算法的“组行进多维数据集”(GMC)算法

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Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.
机译:尽管Marching Cube(MC)算法在显示基于体素的对象的图像方面非常流行,但通常认为其缓慢的表面提取过程是其主要缺点之一。有人指出,对于原始的MC算法,我们可以将顶点计算限制为每个顶点一次,以加快曲面提取过程,但是并未提及如何有效地完成此过程。这些MC顶点的重用也没有在文献中得到认真研究。在本文中,我们提出了一种“ Group Marching Cube”(GMC)算法,以减少顶点识别过程(这是表面提取过程的一部分)所需的时间。由于等值面的大多数三角形顶点由许多MC三角形共享,因此顶点识别过程可以避免在所得三角形数据的顶点数组中重复顶点。 MC算法通常通过文献中提出并由许多软件系统使用的哈希表机制来完成。我们提出的GMC算法同时考虑了一组体素以用于MC算法的应用,以探索原始MC算法的有趣特征(文献中未讨论)。根据我们的实验,对于具有超过100万个顶点的对象,GMC算法比使用哈希表的算法快3到10倍以上。 GMC的另一个重要优点是它与其他可加速MC算法的算法兼容。总之,原始MC算法的整体性能得到了进一步提升。

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