Computer vision and machine learning can be used in cultural heritage to augment the experience of visitors during the exploration of the cultural site, as well as to assist its management. To achieve such goals, two fundamental tasks should be addressed, i.e., localizing visitors and recognizing the observed artworks. Wearable cameras offer a convenient setting to address both tasks through the analysis of images acquired from the visitors’ points of view. However, the engineering of approaches to address such tasks generally requires large amounts of labeled data. We propose a tool which can be used to collect and automatically label synthetic visual data suitable to study image-based localization and artwork detection. The tool simulates a virtual agent navigating the 3D model of a real cultural site and automatically captures video frames along with the related ground truth camera poses and semantic masks indicating the position of artworks. We generate a dataset of synthetic images starting from the 3D model of a museum located in Siracusa, Italy. The experiments suggest that the proposed tool allows to drastically reduce the effort needed to collect and label data, providing a means to generate large-scale datasets suitable to study localization and artwork detection in cultural sites.

计算机视觉和机器学习可用于文化遗产，以在参观文化遗址时增加游客的体验，并协助对其进行管理。为了实现这些目标，应该解决两个基本任务，即定位访客并识别观察到的艺术品。可穿戴式摄像机提供了一种方便的设置，可以通过分析从访问者角度获取的图像来解决这两项任务。然而，解决这些任务的方法的工程设计通常需要大量的标记数据。我们提出了一种工具，该工具可用于收集和自动标记适合用于研究基于图像的定位和艺术品检测的合成视觉数据。该工具模拟在真实文化场所的3D模型中导航的虚拟代理，并自动捕获视频帧以及相关的地面真相相机姿势和表示艺术品位置的语义蒙版。我们从位于意大利锡拉库扎的博物馆的3D模型开始生成合成图像的数据集。实验表明，所提出的工具可以大大减少收集和标记数据所需的工作量，从而提供了一种生成适合于研究文化遗址中的本地化和艺术品检测的大规模数据集的方法。