SYNTHETIC DATA GENERATION AND TESTING FOR THE SEMANTIC SEGMENTATION OF HERITAGE BUILDINGS

Abstract

Over the past decade, the use of machine learning and deep learning algorithms to support 3D semantic segmentation of point clouds has significantly increased, and their impressive results has led to the application of such algorithms for the semantic modeling of heritage buildings. Nevertheless, such applications still face several significant challenges, caused in particular by the high number of training data required during training, by the lack of specific data in the heritage building scenarios, and by the time-consuming operations to data collection and annotation. This paper aims to address these challenges by proposing a workflow for synthetic image data generation in heritage building scenarios. Specifically, the procedure allows for the generation of multiple rendered images from various viewpoints based on a 3D model of a building. Additionally, it enables the generation of per-pixel segmentation maps associated with these images. In the first part, the procedure is tested by generating a synthetic simulation of a real-world scenario using the case study of Spedale del Ceppo. In the second part, several experiments are conducted to assess the impact of synthetic data during training. Specifically, three neural network architectures are trained using the generated synthetic images, and their performance in predicting the corresponding real scenarios is evaluated.