Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911442284838912 |
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| author | Rubinstein, Jacob Donaty, Avi Engel, Don |
| author_facet | Rubinstein, Jacob Donaty, Avi Engel, Don |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11314 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking Rubinstein, Jacob Donaty, Avi Engel, Don Computer Vision and Pattern Recognition Graphics 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. |
| title | Advancing Digital Twin Generation Through a Novel Simulation Framework and Quantitative Benchmarking |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2602.11314 |