The understanding of material appearance perception is a complex problem due to interactions between material reflectance, surface geometry, and illumination. Recently, Serrano et al. collected the largest dataset to date with subjective ratings of material appearance attributes, including glossiness, metallicness, sharpness and contrast of reflections. In this work, we make use of their dataset to investigate for the first time the impact of the interactions between illumination, geometry, and eight different material categories in perceived appearance attributes. After an initial analysis, we select for further analysis the four material categories that cover the largest range for all perceptual attributes: fabric, plastic, ceramic, and metal. Using a cumulative link mixed model (CLMM) for robust regression, we discover interactions between these material categories and four representative illuminations and object geometries. We believe that our findings contribute to expanding the knowledge on material appearance perception and can be useful for many applications, such as scene design, where any particular material in a given shape can be aligned with dominant classes of illumination, so that a desired strength of appearance attributes can be achieved.

由于材料反射率、表面几何形状和照明之间的相互作用，对材料外观感知的理解是一个复杂的问题。最近，塞拉诺等人。收集了迄今为止最大的数据集，对材料外观属性进行主观评价，包括光泽度、金属度、锐度和反射对比度。在这项工作中，我们利用他们的数据集首次研究了照明、几何和八种不同材料类别之间相互作用对感知外观属性的影响。在初步分析之后，我们选择涵盖所有感知属性最大范围的四种材料类别进行进一步分析：织物、塑料、陶瓷和金属。使用累积链接混合模型 (CLMM) 进行稳健回归，我们发现这些材料类别与四种代表性照明和对象几何形状之间的相互作用。我们相信我们的发现有助于扩展材料外观感知的知识，并且可用于许多应用，例如场景设计，其中给定形状的任何特定材料都可以与主要照明类别对齐，以便获得所需的强度可以实现外观属性。