一种高性能的SurfaceNets离散等值面提取算法
A High-Performance SurfaceNets Discrete Isocontouring Algorithm
AI总结：
针对传统离散等值面提取算法并行性不足的问题，研究借鉴Flying Edges算法理念设计并行SurfaceNets算法，通过双缓冲平滑保证确定性，性能较串行算法快1-2个数量级，已开源集成至VTK系统。
摘要：
等值面提取是应用最广泛的可视化技术之一。然而，许多主流的等值面提取算法诞生于多核共享内存计算系统等普及的并行方法出现之前。随着数据规模和计算负载的不断增长，必须重新设计这类算法，以充分利用当下可用的更强计算能力。为此，我们重新设计了SurfaceNets算法，这是一种功能强大的技术，常用于对分割标签图这类非连续的离散体数据标量场进行等值面提取。标签图在医学计算和生物分析中十分常见，应用范围涵盖解剖图谱构建、脑连接组学等诸多领域。\n这种全新的并行SurfaceNets算法借鉴了高性能的Flying Edges连续等值面提取算法的设计理念，包含两个基本步骤：表面提取和后续的约束平滑，二者均沿体数据边进行并行化处理，并采用双缓冲平滑方法以保证结果的确定性。该算法可在单次执行中提取并平滑多个分割对象，生成由三角形或四边形构成的多边形网格，且相邻对象之间完全共享顶点和多边形。在小核数的商用CPU硬件上，其性能通常比当前的离散等值面提取串行算法快1到2个数量级。我们在五组不同的数据集上验证了该算法的有效性，数据集包括人体躯干和大脑图谱、小鼠脑分割数据以及电镜连接组学数据。该软件目前以宽松的开源许可证发布在VTK可视化系统中。
英文摘要：
Isocontouring is one of the most widely used visualization techniques. However, many popular contouring algorithms were created prior to the advent of ubiquitous parallel approaches, such as multi-core, shared memory computing systems. With increasing data sizes and computational loads, it is essential to reimagine such algorithms to leverage the increased computing capabilities available today. To this end we have redesigned the SurfaceNets algorithm, a powerful technique which is often employed to isocontour non-continuous, discrete, volumetric scalar fields such as segmentation label maps. Label maps are ubiquitous to medical computing and biological analysis, used in applications ranging from anatomical atlas creation to brain connectomics. This novel Parallel SurfaceNets algorithm has been redesigned using concepts from the high-performance Flying Edges continuous isocontouring algorrithm. It consists of two basic steps, surface extraction followed by constrained smoothing, parallelized over volume edges and employing a double-buffering smoothing approach to guarantee determinism. The algorithm can extract and smooth multiple segmented objects in a single execution, producing a polygonal (triangular/quadrilateral) mesh with points and polygons fully shared between neighboring objects. Performance is typically one to two orders of magnitude faster than the current sequential algorithms for discrete isosurface extraction on small core-count commodity CPU hardware. We demonstrate the effectiveness of the algorithm on five different datasets including human torso and brain atlases, mouse brain segmentation, and electron microscopy connectomics. The software is currently available under a permissive, open source license in the VTK visualization system.
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