The creation of digital replicas of physical objects has valuable applications for the preservation and dissemination of tangible cultural heritage. However, existing methods are often slow, expensive, and require expert knowledge. We propose a pipeline to generate a 3D replica of a scene using only RGB images (e.g. photos of a museum) and then extract a model for each item of interest (e.g. pieces in the exhibit). We do this by leveraging the advancements in novel view synthesis and Gaussian Splatting, modified to enable efficient 3D segmentation. This approach does not need manual annotation, and the visual inputs can be captured using a standard smartphone, making it both affordable and easy to deploy. We provide an overview of the method and baseline evaluation of the accuracy of object segmentation.
创建物理对象的数字复制品在保护和传播有形文化遗产方面具有重要的应用。然而，现有的方法通常速度慢、成本高，并且需要专业知识。我们提出了一种管道，仅使用RGB图像（如博物馆的照片）生成场景的三维复制品，并随后提取每个感兴趣物体（如展览中的展品）的模型。我们通过利用新视角合成和高斯分布（Gaussian Splatting）的进展，修改这些技术以实现高效的三维分割。此方法无需手动注释，视觉输入可以通过普通智能手机捕捉，使其既经济又易于部署。我们提供了该方法的概述以及物体分割精度的基准评估。