Paper Info
Title
Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage.
Abstract
In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.
Year
DOI
Venue
2020
10.3390/rs12061005
REMOTE SENSING
Keywords
DocType
Volume
classification,semantic segmentation,Digital Cultural Heritage,Point Clouds,Deep Learning
Journal
12
Issue
Citations 
PageRank 
6
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Roberto Pierdicca16112.19
Marina Paolanti21012.39
Francesca Matrone300.34
Massimo Martini400.34
Christian Morbidoni528937.76
Eva Savina Malinverni6398.03
Emanuele Frontoni724847.04
Andrea Lingua8477.50