Open AccessGENERATIVE NETWORKS FOR POINT CLOUD GENERATION IN CULTURAL HERITAGE DOMAIN
Author(s) -
Massimo Martini,
Roberto Pierdicca,
Marina Paolanti,
Ramona Quattrini,
Eva Savina Malinverni,
Emanuele Frontoni
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.4995/arqueologica9.2021.12101
Subject(s) - point cloud , computer science , segmentation , matching (statistics) , artificial intelligence , domain (mathematical analysis) , cultural heritage , point (geometry) , divergence (linguistics) , generative grammar , process (computing) , generative model , deep learning , data mining , machine learning , geography , mathematics , mathematical analysis , linguistics , statistics , philosophy , geometry , archaeology , operating system
In the Cultural Heritage (CH) domain, the semantic segmentation of 3D point clouds with Deep Learning (DL) techniques allows to recognize historical architectural elements, at a suitable level of detail, and hence expedite the process of modelling historical buildings for the development of BIM models from survey data. However, it is more difficult to collect a balanced dataset of labelled architectural elements for training a network. In fact, the CH objects are unique, and it is challenging for the network to recognize this kind of data. In recent years, Generative Networks have proven to be proper for generating new data. Starting from such premises, in this paper Generative Networks have been used for augmenting a CH dataset. In particular, the performances of three state-of-art Generative Networks such as PointGrow, PointFLow and PointGMM have been compared in terms of Jensen-Shannon Divergence (JSD), the Minimum Matching Distance-Chamfer Distance (MMD-CD) and the Minimum Matching Distance-Earth Mover’s Distance (MMD-EMD). The objects generated have been used for augmenting two classes of ArCH dataset, which are columns and windows. Then a DGCNN-Mod network was trained and tested for the semantic segmentation task, comparing the performance in the case of the ArCH dataset without and with augmentation.