The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 223–228, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-223-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 223–228, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-223-2021

  28 Jun 2021

28 Jun 2021

A COMPARATIVE STUDY OF POINT CLOUDS SEMANTIC SEGMENTATION USING THREE DIFFERENT NEURAL NETWORKS ON THE RAILWAY STATION DATASET

Y. A. Lumban-Gaol1,2, Z. Chen1, M. Smit1, X. Li1, M. A. Erbaşu1, E. Verbree1, J. Balado1, M. Meijers1, and N. van der Vaart3 Y. A. Lumban-Gaol et al.
  • 1Faculty of Architecture and the Built Environment, Delft University of Technology, The Netherlands
  • 2Geospatial Information Agency (BIG), Jl. Raya Jakarta-Bogor Cibinong, Indonesia
  • 3Esri Nederland, The Netherlands

Keywords: Point Clouds, Deep learning, Indoor Scene, Semantic Segmentation, Railway Station

Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.