Related papers: Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking

Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking

URL: http://arxiv.org/abs/2602.11314v1
Date: Wed, 11 Feb 2026 19:38:00 GMT
Title: Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking
Authors: Jacob Rubinstein, Avi Donaty, Don Engel,
Abstract summary: We present a novel pipeline for generating synthetic images from high-quality 3D models.<n>This enables a variety of quantifiable experiments which can compare ground-truth knowledge virtual camera parameters and virtual objects.
Score: 2.547746057551682
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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