Digital Twin Generation from Visual Data: A Survey

• URL: http://arxiv.org/abs/2504.13159v1
• Date: Thu, 17 Apr 2025 17:57:41 GMT
• Title: Digital Twin Generation from Visual Data: A Survey
• Authors: Andrew Melnik, Benjamin Alt, Giang Nguyen, Artur Wilkowski, Maciej Stefańczyk, Qirui Wu, Sinan Harms, Helge Rhodin, Manolis Savva, Michael Beetz,
• Abstract summary: Digital twins can be used for robotics application, media content creation, or design and construction works.<n>We analyze various approaches, including 3D Gaussian Splatting, generative in-painting, semantic segmentation, and foundation models.<n>This survey aims to provide a comprehensive overview of state-of-the-art methodologies and their implications for real-world applications.
• Score: 24.812547645957924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This survey explores recent developments in generating digital twins from videos. Such digital twins can be used for robotics application, media content creation, or design and construction works. We analyze various approaches, including 3D Gaussian Splatting, generative in-painting, semantic segmentation, and foundation models highlighting their advantages and limitations. Additionally, we discuss challenges such as occlusions, lighting variations, and scalability, as well as potential future research directions. This survey aims to provide a comprehensive overview of state-of-the-art methodologies and their implications for real-world applications. Awesome list: https://github.com/ndrwmlnk/awesome-digital-twins

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