Paint manufacturers strive to introduce unique visual effects to coatings in order to visually communicate functional properties of products using value-added, customized design. However, these effects often feature complex, angularly dependent, spatially-varying behavior, thus representing a challenge in digital reproduction. In this paper we analyze several approaches to capturing spatially-varying appearances of effect coatings. We compare a baseline approach based on a bidirectional texture function (BTF) with four variants of half-difference parameterization. Through a psychophysical study, we determine minimal sampling along individual dimensions of this parameterization. We conclude that, compared to BTF, bivariate representations better preserve visual fidelity of effect coatings, better characterizing near-specular behavior and significantly the restricting number of images which must be captured.

中文翻译：

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基于图像的效果涂料外观获取

涂料制造商努力为涂料引入独特的视觉效果，以便使用增值的定制设计在视觉上传达产品的功能特性。但是，这些效果通常具有复杂的，角度相关的，空间变化的行为，因此代表了数字再现的挑战。在本文中，我们分析了几种捕获效果涂料空间变化外观的方法。我们将基于双向纹理函数（BTF）的基线方法与半差分参数化的四个变体进行比较。通过心理物理学研究，我们确定了该参数化各个维度上的最小抽样。我们得出的结论是，与BTF相比，双变量表示可以更好地保留效果涂层的视觉保真度，