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

翻译：本综述探讨了从视觉数据生成数字孪生的最新进展。这些数字孪生——物理资产的虚拟三维副本——可应用于机器人学、媒体内容创作、设计或施工流程。我们分析了一系列方法，包括3D Gaussian Splatting、生成式修复、语义分割以及基础模型，并重点阐述了它们各自的优势与局限。此外，我们讨论了遮挡、光照变化和可扩展性等关键挑战，同时指出了当前研究的空白、发展趋势以及未来研究方向。总体而言，本综述旨在全面概述最先进的方法论及其在现实世界应用中的意义。Awesome Digital Twin: https://awesomedigitaltwin.github.io