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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.13159 |
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| _version_ | 1866911451394867200 |
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| author | Melnik, Andrew Alt, Benjamin Nguyen, Giang Wilkowski, Artur Stefańczyk, Maciej Wu, Qirui Harms, Sinan Rhodin, Helge Savva, Manolis Beetz, Michael |
| author_facet | Melnik, Andrew Alt, Benjamin Nguyen, Giang Wilkowski, Artur Stefańczyk, Maciej Wu, Qirui Harms, Sinan Rhodin, Helge Savva, Manolis Beetz, Michael |
| contents | This survey examines recent advances in generating digital twins from visual data. These digital twins - virtual 3D replicas of physical assets - can be applied to robotics, media content creation, design or construction workflows. We analyze a range of approaches, including 3D Gaussian Splatting, generative inpainting, semantic segmentation, and foundation models, highlighting their respective advantages and limitations. In addition, we discuss key challenges such as occlusions, lighting variations, and scalability, as well as identify gaps, trends, and directions for future research. Overall, this survey aims to provide a comprehensive overview of state-of-the-art methodologies and their implications for real-world applications. Awesome Digital Twin: https://awesomedigitaltwin.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital Twin Generation from Visual Data: A Survey Melnik, Andrew Alt, Benjamin Nguyen, Giang Wilkowski, Artur Stefańczyk, Maciej Wu, Qirui Harms, Sinan Rhodin, Helge Savva, Manolis Beetz, Michael Computer Vision and Pattern Recognition This survey examines recent advances in generating digital twins from visual data. These digital twins - virtual 3D replicas of physical assets - can be applied to robotics, media content creation, design or construction workflows. We analyze a range of approaches, including 3D Gaussian Splatting, generative inpainting, semantic segmentation, and foundation models, highlighting their respective advantages and limitations. In addition, we discuss key challenges such as occlusions, lighting variations, and scalability, as well as identify gaps, trends, and directions for future research. Overall, this survey aims to provide a comprehensive overview of state-of-the-art methodologies and their implications for real-world applications. Awesome Digital Twin: https://awesomedigitaltwin.github.io |
| title | Digital Twin Generation from Visual Data: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.13159 |