The generation of 3D models from real-world objects has often been accomplished through photogrammetry, i.e., by taking 2D photos from a variety of perspectives and then triangulating matched point-based features to create a textured mesh. Many design choices exist within this framework for the generation of digital twins, and differences between such approaches are largely judged qualitatively. Here, we present and test a novel pipeline for generating synthetic images from high-quality 3D models and programmatically generated camera poses. This enables a wide variety of repeatable, quantifiable experiments which can compare ground-truth knowledge of virtual camera parameters and of virtual objects against the reconstructed estimations of those perspectives and subjects.
@article{arxiv.2602.11314,
title = {Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking},
author = {Jacob Rubinstein and Avi Donaty and Don Engel},
journal= {arXiv preprint arXiv:2602.11314},
year = {2026}
}