An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

The joint role of geometry and illumination on material recognition

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly nonlinear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly nonlinear models and humans may use similar high-level factors for material recognition tasks

Material Visualisation for Virtual Reality: The Perceptual Investigations

Material representation plays a significant role in design visualisation and evaluation. On one hand, the simulated material properties determine the appearance of product prototypes in digitally rendered scenes. On the other hand, those properties are perceived by the viewers in order to make important design decisions. As an approach to simulate a more realistic environment, Virtual Reality (VR) provides users a vivid impression of depth and embodies them into an immersive environment. However, the scientific understanding of material perception and its applications in VR is still fairly limited. This leads to this thesis’s research question on whether the material perception in VR is different from that in traditional 2D displays, as well as the potential of using VR as a design tool to facilitate material evaluation.       This thesis is initiated from studying the perceptual difference of rendered materials between VR and traditional 2D viewing modes. Firstly, through a pilot study, it is confirmed that users have different perceptual experiences of the same material in the two viewing modes. Following that initial finding, the research investigates in more details the perceptual difference with psychophysics methods, which help in quantifying the users’ perceptual responses. Using the perceptual scale as a measuring means, the research analyses the users’ judgment and recognition of the material properties under VR and traditional 2D display environments. In addition, the research also elicits the perceptual evaluation criteria to analyse the emotional aspects of materials. The six perceptual criteria are in semantic forms, including rigidity, formality, fineness, softness, modernity, and irregularity.       The results showed that VR could support users in making a more refined judgment of material properties. That is to say, the users perceive better the minute changes of material properties under immersive viewing conditions. In terms of emotional aspects, VR is advantageous in signifying the effects induced by visual textures, while the 2D viewing mode is more effective for expressing the characteristics of plain surfaces. This thesis has contributed to the deeper understanding of users’ perception of material appearances in Virtual Reality, which is critical in achieving an effective design visualisation using such a display medium