# Digital Twin Generation from Visual Data: A Survey

Source: [Digital Twin Generation from Visual Data: A Survey](<https://www.sota2.com/research/paper/68fa781a1e209131cea76ce6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-19T05:00:04.488Z

Authors: Andrew Melnik, Benjamin Alt, Giang Nguyen, Artur Wilkowski, Maciej Stefa\\'nczyk, Qirui Wu, Sinan Harms, Helge Rhodin, Manolis Savva, Michael Beetz

Published: 2025-04-17T17:57:41.000Z

## Abstract

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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.13159>)

[PDF](<https://arxiv.org/pdf/2504.13159.pdf>)