Fast Computing Adaptively Sampled Distance Field on GPU
Fast Computing Adaptively Sampled Distance Field on GPU
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DOI：
10.2312/pe/pg/pg2011short/025-030
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
K. Yin;Youquan Liu;E. Wu
中科院分区：
文献类型：
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作者：
K. Yin;Youquan Liu;E. Wu
在本文中，我们提出了一种计算大型三角形网格有符号距离场的有效方法，该方法可在GPU加速下交互运行。由于GPU上缺乏灵活的指针寻址，我们设计了一种新颖的多层哈希表，将体素/三角形重叠对组织为二元组，这种策略提供了一种有效的存储和访问方式。基于通用的八叉树结构思想，给出了一种基于GPU的八叉树结构来生成用于计算到三角形网格最短距离的采样点。将采样点分为三类在性能和精度之间提供了良好的权衡，并且在GPU上实现该算法时，这些采样点也被组织成块，以便线程间共享三角形以节省带宽。最后，我们使用伪法线方法演示了对一些典型大型三角形网格的全局有符号距离场的高效计算。与先前的工作相比，我们的算法在性能上相当快。
In this paper we present an efficient method to compute the signed distance field for a large triangle mesh, which can run interactively with GPU accelerated. Restricted by absence of flexible pointer addressing on GPU, we design a novel multi-layer hash table to organize the voxel/triangle overlap pairs as two-tuples, such strategy provides an efficient way to store and access. Based on the general octree structure idea, a GPU-based octree structure is given to generate the sample points which are used to calculate the shortest distance to the triangle mesh. Classifying sample points into three types provides a well tradeoff between performance and precision, and when implementing the algorithm on GPU, these samples are also organized into blocks to share the triangles among threads to save bandwidth. Finally we demonstrate efficient calculation of the global signed distance field for some typical large triangle meshes with pseudo-normal method. Compared to previous work, our algorithm is quite fast in performance.