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A data-dependent gradient quantization scheme for the acceleration of volume rendering

机译:一种数据依赖性梯度量化方案,用于加速卷渲染

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Volume rendering requires the use of gradient information used as surface normal information, for application of lighting models. However, for interactive applications on-the-fly calculation of gradients is too slow. The common solution to this problem is to quantize gradients of trivariate scalar fields and pre-compute a look-up table prior to the application of a volume rendering method. A number of techniques have been proposed for the quantization of normal vectors, but few have been applied to or adapted for the purpose of volume rendering. We describe an new data-dependent method to quantize gradients using an even number of vectors in a table. The quantization scheme we use is based on a tessellation of the unit sphere. This tessellation represents an "optimally" distributed set of unit normal vectors. Staring with a random tessellation, we optimize the size and distribution of the tiles (on the unit sphere) with a simulated annealing approach.
机译:音量渲染需要使用用作表面正常信息的梯度信息,以应用照明模型。但是,对于梯度的互动应用程序,梯度的计算得太慢。该问题的常见解决方案是在应用卷呈现方法之前量化跨越标量字段的梯度并在查找查找表之前进行预先计算。已经提出了许多用于量化正常载体的技术,但是少量应用于或适于体积渲染的目的。我们描述了一种新的数据依赖方法,用于使用表中的偶数矢量来量化梯度。我们使用的量化方案基于单位球体的曲面细分。该曲面细分表示“最佳”分布式的单位正常载体组。凝视随机镶嵌,我们利用模拟退火方法优化瓷砖(在单位球体上)的尺寸和分布。

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