  Training defect detection algorithms for visual surface inspection systems
requires a large and representative set of training data. Often there is not
enough real data available which additionally cannot cover the variety of
possible defects. Synthetic data generated by a synthetic visual surface
inspection environment can overcome this problem. Therefore, a digital twin of
the object is needed, whose micro-scale surface topography is modeled by
texture synthesis models. We develop stochastic texture models for sandblasted
and milled surfaces based on topography measurements of such surfaces. As the
surface patterns differ significantly, we use separate modeling approaches for
the two cases. Sandblasted surfaces are modeled by a combination of data-based
texture synthesis methods that rely entirely on the measurements. In contrast,
the model for milled surfaces is procedural and includes all process-related
parameters known from the machine settings.
