Abstract
Abstract. Although point clouds are characterized as a type of unstructured data, timestamp attribute can structure point clouds into scanlines and shape them into a time signal. The present work studies the transformation of the street point cloud into a time signal based on the Z component for the semantic segmentation using Long Short-Term Memory (LSTM) networks. The experiment was conducted on the point cloud of a real case study. Several training sessions were performed changing the Level of Detail of the classification (coarse level with 3 classes and fine level with 11 classes), two levels of network depth and the use of weighting for the improvement of classes with low number of points. The results showed high accuracy, reaching at best 97.3% in the classification with 3 classes (ground, buildings, and objects) and 95.7% with 11 classes. The distribution of the success rates was not the same for all classes. The classes with the highest number of points obtained better results than the others. The application of weighting improved the classes with few points at the expense of the classes with more points. Increasing the number of hidden layers was shown as a preferable alternative to weighting. Given the high success rates and a behaviour of the LSTM consistent with other Neural Networks in point cloud processing, it is concluded that the LSTM is a feasible alternative for the semantic segmentation of point clouds transformed into time signals.
Highlights
Point clouds are known to be a typical example of unstructured data
The aim of this work is to evaluate the semantic segmentation of street point clouds considering them as time signals and applying Recurrent Neural Networks (RNN)
The first experiments of semantically segmenting an Mobile Laser Scanning (MLS) street point cloud through the conversion into a time signal and a Long Short-Term Memory (LSTM)
Summary
Point clouds are known to be a typical example of unstructured data. In a detailed analysis of the distribution of points on surfaces in raw point clouds, certain patterns related to geometry and scanning time can be appreciated (Figure 1). These patterns match the scanlines and are identified in terrestrial, mobile, or airborne laser scanning. Scanlines are lines of successive points that form a point cloud. The structuring of the point cloud in scanlines has shown its usefulness in segmentation (Chu et al, 2017; Honma et al, 2019; Wang et al, 2016), ground detection (Che and Olsen, 2017; Chu et al, 2019), road edge detection (Honma et al, 2020), indoor space subdivision (Zheng et al, 2018) and modelling (Lin and Hyyppä, 2011)
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