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FAST IMPLICIT KD-TREES: ACCELERATED ISOSURFACE RAY TRACING AND MAXIMUM INTENSITY PROJECTION FOR LARGE SCALAR FIELDS

机译:快速隐式KD树:大标量场的加速等面射线跟踪和最大强度投射

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Many scientific data sets are 3D or 4D scalar fields, for which typically isosurface- and volume visualization methods are used to extract information. These data sets are either massively complex (e.g., seismic data sets), or steadily increasing in size due to the permanently improving resolutions of different 3D scanners (e.g., CT- and MRT-scanners) or calculation results (e.g., CFD-simulations). Only algorithms that scale well to data set complexity are suited to visualize those increasing data sets. Isosurface ray tracing and maximum intensity projection (MIP) accelerated through implicit KD-trees have a logarithmic dependency between visualization time and scene size, making them ideal algorithms for the visualization of massively complex scalar fields. Furthermore is ray tracing efficiently parallelized on the more and more commonly used shared memory machines (e.g., desktop machines with several multicore processors) and may be used to realize advanced shading effects like shadows and reflections. We introduce new optimized implicit KD-trees which allow on today's desktop computers interactive iso-surfacing and MIP of data sets that are bigger than one half of the machine's main memory.
机译:许多科学数据集是3D或4D标量字段,通常使用等值面和体积可视化方法来提取信息。这些数据集要么非常复杂(例如地震数据集),要么由于不同3D扫描仪(例如CT和MRT扫描仪)或计算结果(例如CFD模拟)的分辨率不断提高而尺寸不断增加。 。只有能够很好地适应数据集复杂性的算法才适合可视化那些不断增长的数据集。通过隐式KD树加速的等值面射线追踪和最大强度投影(MIP)在可视化时间和场景大小之间具有对数相关性,使其成为可视化大型复杂标量场的理想算法。此外,在越来越多的共享存储机器(例如,具有多个多核处理器的台式机)上有效地并行化了光线跟踪,并且可以用于实现高级阴影效果,例如阴影和反射。我们引入了新的优化的隐式KD树,这些树允许在当今的台式计算机上进行交互式iso覆盖和MIP数据集,这些数据集大于计算机主内存的一半。

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