Mao, RunzhouGarth, ChristophGospodnetic, PetraWang, BeibeiWilkie, Alexander2025-06-202025-06-202025978-3-03868-292-91727-3463https://doi.org/10.2312/sr.20251188https://diglib.eg.org/handle/10.2312/sr20251188Automated defect detection is critical for quality control, but collecting and annotating real-world defect images remains costly and time-consuming, motivating the use of synthetic data. Existing methods such as geometry-based modeling, normal maps, and image-based approaches often struggle to balance realism, efficiency, and scalability. We propose a procedural method for synthesizing small-scale surface defects using gradient-based bump mapping and triplanar projection. By perturbing surface normals at shading time, our approach enables parameterized control over diverse scratch and dent patterns, while avoiding mesh edits, UV mapping, or texture lookup. It also produces pixel-accurate defect masks for annotation. Experimental results show that our method achieves comparable visual quality to geometry-based modeling, with lower computational overhead and improved surface continuity over static normal maps. The method offers a lightweight and scalable solution for generating high-quality training data for industrial inspection tasks.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> RenderingComputing methodologiesRendering10.2312/sr.2025118814 pages