Inverse rendering of a digital twin for visual inspection via anomaly detection

This article introduces a novel approach for automated visual inspection through inverse rendering of a digital twin, enhancing the detection of material defects. Our method generates reference images via ray tracing, simulating the actual appearance of products while accommodating inherent variations in manufacturing. By employing inverse rendering and automatic differentiation, we adjust the digital twin’s parameters to align the simulated images with those captured by the inspection system. This optimization process effectively minimizes discrepancies, allowing us to compute difference images that primarily highlight material defects. Our experiments validate the approach, demonstrating significant improvements in signal-to-noise ratios and defect visibility compared to conventional methods. The potential applications of our approach are particularly notable in the context of Industry 4.0, especially in lot size 1 scenarios, where high product variability complicates traditional inspection processes. By leveraging the availability of CAD models, our method offers a practical solution for adaptive inspection frameworks that can efficiently handle diverse product designs.