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

TL;DR

This paper introduces a new simulation framework for generating synthetic images from 3D models, enabling quantitative benchmarking of digital twin generation methods and improving the reproducibility of experiments.

Contribution

A novel pipeline for creating synthetic images from 3D models with programmable camera poses, facilitating repeatable and quantifiable digital twin benchmarking.

Findings

Enables precise comparison of ground-truth and estimated camera parameters.

Supports reproducible experiments in digital twin generation.

Provides a framework for quantitative evaluation of photogrammetry approaches.

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.