A "group marching cube" (GMC) algorithm for speeding up the marching cube algorithm

Lih Shyang Chen, Young Jinn Lay, Je Bin Huang, Yan De Chen, Ku Yaw Chang, Shao Jer Chen

研究成果: Article同行評審

摘要

Although theMarching 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 isosurface 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 theMC algorithm. Together, the overall performance of the originalMC algorithm is promoted even further.

原文English
頁(從 - 到)1289-1298
頁數10
期刊IEICE Transactions on Information and Systems
E94-D
發行號6
DOIs
出版狀態Published - 2011 六月

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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