Treffer: A Physically-Based Model for Rendering Realistic Scratches.

Individually visible scratches, also called isolated scratches, are very common in real world surfaces. Although their microgeometry is not visible, they are individually perceptible by the human eye, lying into a representation scale between BRDF and texture. In order to simulate this kind of scratches in synthetic images we need to know their position over the surface (texture scale), so we can determine where to use the specific scratch BRDF instead of the ordinary surface BRDF. Computing the BRDF of a scratch is difficult because it depends on the scratch's invisible microgeometry. In this paper, we propose a new physically based model to derive this microgeometry by simulating the formation process of scratches. We allow specifying intuitively the parameters involved in the process such as the scratching tool, the penetration forces, and the material properties of the object. From these parameters, we derive the microgeometries of the scratches by taking into account the real behaviour of the process. This behaviour has been determined by analysing existing models in the field of materials engineering and some ``scratch tests'' that we performed on metals. Our method has the advantages of easily simulating scratches with a wide range of microgeometries and taking into account the variability of their microgeometry along the scratch path. Another contribution is related to the location of the scratches over the surface. Instead of using an image of the paths as in previous work, we present a new representation based on curves defining the paths. This offers an independence on the image resolution or the distance from the observer and accurately provides the scratch direction in order to compute scratch BRDFs. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Color, shading, shadowing, and texture. [ABSTRACT FROM AUTHOR]

Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)