Multilevel streaming for out-of-core surface reconstruction
Multilevel streaming for out-of-core surface reconstruction
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DOI：
10.2312/sgp/sgp07/069-078
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Matthew Bolitho;M. Kazhdan;R. Burns;Hugues Hoppe
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文献类型：
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作者：
Matthew Bolitho;M. Kazhdan;R. Burns;Hugues Hoppe
从大量扫描点的集合重建表面通常需要核外技术，并且大多数此类技术涉及对数据错误没有弹性的局部计算。我们表明，基于泊松的重建方案，它认为在一个全球性的分析，可以有效地执行在有限的内存中使用流框架。具体来说，我们引入了一个多层次的流表示，它使有效的遍历稀疏八叉树同时推进通过多个流，每个八叉树级别。值得注意的是，对于我们的重建应用程序，一个足够精确的解决方案，以获得全局线性系统使用一个单一的迭代的瀑布多重网格，它可以在一个单一的多流通过评估。我们在几个大型数据集上展示了可扩展的性能。
Reconstruction of surfaces from huge collections of scanned points often requires out-of-core techniques, and most such techniques involve local computations that are not resilient to data errors. We show that a Poisson-based reconstruction scheme, which considers all points in a global analysis, can be performed efficiently in limited memory using a streaming framework. Specifically, we introduce a multilevel streaming representation, which enables efficient traversal of a sparse octree by concurrently advancing through multiple streams, one per octree level. Remarkably, for our reconstruction application, a sufficiently accurate solution to the global linear system is obtained using a single iteration of cascadic multigrid, which can be evaluated within a single multi-stream pass. We demonstrate scalable performance on several large datasets.