mlsanityData to 14 Oct 2026

Digital Twin Generation from Visual Data: A Survey

arXiv 2504.13159 ↗First posted 17 Apr 2025cs.CVcited by 7

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Abstract (arXiv)

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

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 232 of its 317 references that are in the map. First posted on arXiv 17 Apr 2025.

Builds on

232 of 317 references.

References · 232

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38 more
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50 most cited of 232 All 232 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.