提出新仿真框架，让数字孪生生成可量化对比。
Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking
- 用高精度3D模型生成合成图像，程序化控制相机位姿。
- 实现虚拟相机参数与物体的精确重建，误差可测。
- 适合需要可复现评估的数字孪生研究者。
从真实物体生成3D模型通常通过摄影测量法，即从多个视角拍摄2D照片，通过匹配特征点进行三角化生成带纹理的网格。当前数字孪生生成方法存在多种设计选择，但差异多以定性方式评判。本文提出并测试了一种新流程：从高质量3D模型生成合成图像，并程序化生成相机位姿。该方法支持大量可重复、可量化的实验，能够将虚拟相机参数和虚拟物体的真实信息，与重建结果进行对比，实现对视角和对象估计的精准评估。
原文摘要 · Abstract (English)
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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