Ray tracing and rasterisation to generate speckle pattern images for digital image correlation uncertainty quantification
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The Pyvale Python package [1] aims to serve as a comprehensive toolset designed to handle the entire lifecycle of sensor modelling. It intends to integrate the following functionalities: simulating sensor behaviour, quantifying measurement uncertainty, optimising where sensors are positioned, and providing simulation calibration together with validation. Pyvale has a particular focus on optical sensing, such as Digital Image Correlation (DIC) [2]. As such, there is a need to understand and model the uncertainties (systematic and random) inherent in these imaging systems. In computer graphics, rendering scenes for simulated cameras primarily involves two techniques: rasterisation and ray tracing. While rasterisation is efficient for real-time use in video games and other rendering applications [3], ray tracing provides a much higher level of physical accuracy by simulating complex lighting behaviours like reflections and shadows [4]. As lighting conditions and optical distortions influence the precision of DIC measurements, incorporating these effects is essential as well. This work compares rasterisation and ray tracing techniques to create deformed speckle pattern images from solid mechanics simulations for subsequent processing with DIC engines. Preliminary evaluations focus on comparing the synthetic images generated by both techniques against established benchmarks for DIC. Initial observations suggest that while rasterisation provides a significant advantage in computational speed, ray tracing offers a more nuanced representation of light-surface interactions, particularly in scenarios involving varying illumination. The analysis specifically quantifies how these rendering differences propagate through the DIC algorithms, affecting the resultant displacement and strain fields. The findings of this study aim to provide a framework for selecting rendering engines within the Pyvale package balancing computational cost against accuracy. Ultimately, these results will contribute to a more robust validation pipeline for DIC uncertainty quantification. REFERENCES [1] Pyvale: The python validation engine, https: //computer-aided-validation-laboratory.github. io/pyvale/index.html, release 2025.8.1, MIT License (2025). [2] B. Pan, Digital image correlation for surface deformation measurement: Historical developments, recent advances and future goals (2018). doi:10.1088/1361-6501/aac55b. [3] M. Schutz, B. Kerbl and M. Wimmer. Soft
