The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-4/W1-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W1-2020, 95–102, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W1-2020, 95–102, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020

  03 Sep 2020

03 Sep 2020

GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS

C. Morbidoni, R. Pierdicca, R. Quattrini, and E. Frontoni C. Morbidoni et al.
  • Universitá Politecnica delle Marche, Ancona, Italy

Keywords: Semantic Segmentation, 3D Point Clouds, Terrestrial Laser Scanning, Deep Learning, Radius distance, Dynamic Graph Convolutional Neural Networks, EdgeConv, Historical Buildings

Abstract. Point clouds obtained via Terrestrial Laser Scanning (TLS) surveys of historical buildings are generally transformed into semantically structured 3D models with manual and time-consuming workflows. The importance of automatizing this process is widely recognized within the research community. Recently, deep neural architectures have been applied for semantic segmentation of point clouds, but few studies have evaluated them in the Cultural Heritage domain, where complex shapes and mouldings make this task challenging. In this paper, we describe our experiments with the DGCNN architecture to semantically segment historical buildings point clouds, acquired with TLS. We propose a variation of the original approach where a radius distance based technique is used instead of K-Nearest Neighbors (KNN) to represent the neighborhood of points. We show that our approach provides better results by evaluating it on two real TLS point clouds, representing two Italian historical buildings: the Ducal Palace in Urbino and the Palazzo Ferretti in Ancona.