Abstract
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.
Highlights
Over the last few years, technology has constantly been evolving, and with the constant growth of computational power, ideas from a long time ago have resurfaced to show their worth completely
We focus on point cloud data which is currently less explored than the traditional image-based machine learning
This paper uses three different neural network architectures to evaluate on our dataset, namely PointNet++ (Qi et al, 2017a), PointCNN (Li et al, 2018), and DGCNN (Wang et al, 2019)
Summary
Over the last few years, technology has constantly been evolving, and with the constant growth of computational power, ideas from a long time ago have resurfaced to show their worth completely. With this constant growth of information, the collection of data has increased as well as the detail with which this has been captured. The training data of deep learning are defined by the application. For indoor and outdoor applications, naturally, this implies the use of different data. It does not imply that the indoor scene is inferior to the outdoor scene. We focus on point cloud data which is currently less explored than the traditional image-based machine learning
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