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CHAPTER 1

Introduction

The visual appearance of an object is a complex phenomenon, and to describe it properly it is important to understand how a material interacts with the light. For this purpose, many reflectance functions have been investigated, not only in computer graphics, where it is an active research field aiming to obtain photorealistic renderings, but many other disciplines like physics, optical engineering, computer science, and psychology, where considerable time and resources are being invested in the acquisition and representation of reflectance functions. In fact, material appearance plays an important role in many areas of science and industry:

• Computer Vision (e.g., in object recognition applications)

• Aerospace (e.g., for optimal definition of satellite mirrors reflectance and scattering properties)

• Optical Engineering, Remote-Sensing (e.g., land cover classification, correction of view and illumination angle effects, cloud detection, and atmospheric correction)

• Medical Applications (e.g., diagnostics)

• Art (e.g., 3D printing)

• Applied Spectroscopy (e.g., physical condition of a surface)

• Film, Games, Virtual Reality, Marketing, etc.

The world of technology is evolving and offering new, innovative technology that allows the “synthetic world” to look more and more realistic: as a consequence, digital reproduction of real-world material is growing rapidly. These results are driven by the combined effort of researchers, engineers and artists. However, despite the number of material reproduction techniques, material modeling is still a popular topic in research and industry since there is no straightforward way to digitize materials, and often the acquired data is not consistently reusable.

As mentioned, a challenge in computer graphics is how to handle visual appearance accurately measuring and representing material characteristics from the real-world in order to replicate a material behavior and its interaction with the light. Several models and acquisition techniques suggested by researchers in recent years are often aimed at a particular subset of material properties. Ultimately, this limits the applicability of a method only to a specific class of materials. In fact, many methods and setups can properly deal with only a few classes of materials (e.g., leather, fabric, car paint, wood, plastic, rubber, mirrored surfaces, etc.) and unfortunately there is no universal way to acquire and represent all classes. If we look at the material representation side, it could be challenging to choose which a model. On the other hand, digital artists have been provided with applications that include material models with intuitive control, but generally they offer only few material models clearly identifiable with a known analytic formula in the scientific literature. Wider choice of material models is also available in physically based renderers, but those might be difficult to use and far from intuitive. An additional challenge is that the same material rendered in different applications might appear differently due to light tracing algorithms and other implementation choices. The cost of the material digitization could be high or low, where a high cost is not necessarily better. On the material acquisition side, time and costs often represent additional constraints, since they vary enormously across the available measurement devices, and high acquisition time and cost do not necessarily mean “better” (or worse, for that matter).

1.1 CHALLENGES

A major challenge in computer graphics is how to simply and accurately measure the appearance of material characteristics from real-world objects and implement practical editable synthetic materials accurately matching the appearance of the original. Currently, no up-to-date universal material model that can represent leather, fabric, car paint, wood, plastic, rubber, mirrored surfaces, etc. exists [SDSG13]. A variety of rendering algorithms are used in the software pipeline, resulting in a need for optimized material representations, which requires both a flexible acquisition process and representation methods. Unfortunately, the following challenges still persist:

• there is no widely adopted solution;

• few solutions acquire material models that are good enough for a wide range of commercial applications without significant lor and money;

• there is no standardized material model formats from acquisition setups;

• there is little standardization across renderers, with different renderers supporting subsets of material properties;

• material models are hard to edit by artists;

• acquired material models have a high memory footprint which limits applicability;

• it is not easy to assess the accuracy of a material model or a measurement setup.

1.2 SCOPE OF THE BOOK

This book provides an overview of reflection functions, the state-of-the-art material models and reflection acquisition setups, and could be seen as a beginner’s guide providing existing techniques for material representation and acquisition in computer graphics, aimed to help in selecting the most appropriate reflectance models and measurement technique for a specific problem. The focus is mainly on current Bidirectional Reflectance Distribution Function (BRDF) representations and acquisition setups, although different reflectance functions will be described where appropriate. For better understanding of the reflectance topic, we begin with a brief overview of the existing reflectance functions, provided in Chapter 2.

Selecting a suitable reflectance model to render a virtual material is not a straightforward task since each reflectance model often aims to represent a specific (subset of) properties, with the result that a given model can describe plastic very well but not metals and so on; BRDF models are described in Chapter 3, where we also describe some tools for BRDF visualization and fitting. In this book we also present different acquisition setups, ranging from low to high cost, each with a varying degree of accuracy. Reflectance acquisition setups are described in Chapter 4, where we guide the reader through available techniques, providing pros and cons of each setup.

For relevant background in mathematics we suggest some additional readings, such as Ström et al. [SAA15]; as for the physics background the reader can find more details in [Gla94].