Optimizing exposure times of structured light metrology systems using a digital twin

Structured light metrology provides a precise and efficient digital reconstruction of real-world objects through projected light patterns and a camera system. However, conducting measurements through these systems often requires time-intensive manual setting of input measurement parameters that may not be generalizable across a wide range of objects. Automating the steps involved in optimizing these parameters can significantly improve the quality of measured data. This work presents a digital twin to automate and refine measurements based on ray-tracing simulations that reveal the best (combination of) exposure time(s). Here we also conduct operational verification of this approach by comparing the exposure time search conducted by experienced operators against our tool. More broadly, the optimization metric we developed and implemented is platform-independent and thus can serve to assess any structured light measurement and/or rank competing scanners. Lastly, it is straightforward to extend this approach for light-matter interactions in any system of interest.