Filtering Normal Maps and Creating Multiple Surfaces
Filtering Normal Maps and Creating Multiple Surfaces
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过滤法线贴图并创建多个曲面
DOI：
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发表时间：
1992
期刊：
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通讯作者：
A. Fournier
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文献类型：
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作者：
A. Fournier
“凹凸”贴图是纹理贴图的一种变体，其中纹理信息用于改变表面法线。目前的技术，以预过滤纹理都依赖于这样的事实，即纹理信息可以线性“因子”的着色方程，因此可以预先平均以某种方式。凹凸贴图则不然，这些技术无法正确过滤它们。我们在这里提出了一种技术，通过建立一个金字塔，其中每个级别存储从底层凹凸贴图给出的分布重建的法向量的分布来预过滤凹凸贴图。分布表示为正常向量的少量Phong样扩展的总和。该技术，除了允许凹凸贴图和单个表面描述之间的有效和平滑过渡之外，还产生了多表面的概念，其中单个表面的每个点的特征在于多个法向量。这允许通过对当前局部照明模型的微小修改来描述视觉上复杂的表面。当一个表面具有底层微结构时，掩蔽和自遮蔽是其外观的重要因素。沿着法线的过滤，我们包括掩蔽和自阴影信息的过滤。这是通过计算可见性的极限角度和它们的方差沿着两个纹理轴的法线的重建分布来实现的。这些技术允许任何表面的建模，其微观结构，我们可以几何建模。这包括复杂但熟悉的表面，如各向异性表面，许多编织布和随机表面。
``Bump'''' mapping is a variant of texture mapping where the texture information is used to alter the surface normal. Current techniques to pre-filter textures are all relying on the fact that the texture information can be linearly "factored out" of the shading equation, and therefore can be pre-averaged in some way. This is not the case with bump maps, and those techniques fail to filter them correctly. We propose here a technique to pre-filter bump maps by building a pyramid where each level stores distributions of normal vectors reconstructed from the distribution given by the underlying bump map. The distributions are represented as sums of a small number of Phong-like spreads of normal vectors. The technique, besides allowing an effective and smooth transition between a bump map and a single surface description, gives rise to the concept of a multiple surface, where each point of the single surface is characterized by more than one normal vector. This allows the description of visually complex surfaces by a trivial modification of current local illumination models. When a surface has an underlying microstructure, masking and self-shadowing are important factors in its appearance. Along with the filtering of normals we include the filtering of the masking and self-shadowing information. This is accomplished by computing the limiting angles of visibility and their variance along the two texture axes for the reconstructed distribution of normals. These techniques allow the modeling of any surface whose microstructure we can model geometrically. This includes complex but familiar surfaces such as anisotropic surfaces, many woven cloth, and stochastic surfaces.