The effect of geometry and illumination on appearance perception of different material categories

Abstract

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.

Unsupervised Learning Can Predict Object Properties of Non-Rigid Mirror Objects in Ambiguous Conditions

“…The network's ability to classify objects' glossiness was not significantly affected by contrast analysis. This result was contrary to previous assumptions and the literature which found that high contrast can enhance the perceived objects' glossiness [3,13,14]. Our analysis of the network's decision values exhibited invariance to the contrast levels when making glossiness-related decisions.…”

Observation-driven generation of texture maps depicting dust accumulation over time

“…It has been recognized that material appearance perception is a challenging problem for synthetic, pristine objects, even when these objects are subjected to controlled viewing and illumination conditions [ 11 ]. Although environmental factors, such as the presence of dust, can provide important visual cues and lead to an increase in the perceived level of realism of these objects, from an optics perspective, they may add yet another layer of complexity to the problem.…”

“…y,t V x,y L x,y + M x,y (1 − V x,y L x,y ). (11) In Fig. 8, we provide a visual representation of this final calculation, showing the texture maps and geometric information used by the proposed framework.…”