Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao 266100, China.
Science and Information College, Qingdao Agricultural University, 700 Changcheng Road, Qingdao 266109, China.
Sensors (Basel). 2020 Feb 19;20(4):1135. doi: 10.3390/s20041135.
Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods.
纹理是模拟真实世界场景和在许多应用中提供真实和沉浸式感觉的最重要元素。程序性纹理可以模拟广泛的表面纹理，这有助于新传感器的设计和开发。程序性纹理生成是使用数学模型创建纹理的过程。这些模型的输入可以是一组参数、噪声函数生成的随机值，或者是现有的纹理图像，这些图像可以进一步处理或组合以生成新的纹理。已经提出了许多程序性纹理生成方法，但尚未对它们进行全面调查或比较。在本文中，我们根据生成纹理的特征，对不同的程序性纹理生成方法进行了综述。我们将不同的生成方法分为两类：结构化纹理和非结构化纹理生成方法。使用这些方法和不同的参数值生成示例纹理。此外，我们还根据不同生成模型的滤波和组合，调查了基于后处理的方法。我们还根据它们可以产生的数学函数和纹理样本，对不同的模型进行了分类。最后，设计了一个心理物理实验来识别示例纹理的感知特征。最后，对结果的分析说明了这些方法的优缺点。
