Segmentation-Based 3D Point Cloud Classification on a Large-Scale and Indoor Semantic Segmentation Dataset

Abstract

This paper presents a segmentation-based classification technique for 3D point clouds. This technique is supervised and needs a ground-truth data for the training process. In this work, the Stanford Large-Scale 3D Indoor Spaces (S3DIS) dataset has been used for the classification of points with the segmentation pre-processing. The dataset consists of a huge amount of points and has semantic ground-truth segments (structures and objects). The main problem in this study is to classify raw points according to the predefined objects and structures. For this purpose, each semantic segment in the training part is segmented separately by a novel successful segmentation algorithm at first. The extracted features of each sub-segments resulted from the segmentation of the semantic segments in the training part are trained using the classifier, and a trained model is obtained. Finally, the raw data reserved for testing are segmented using the same segmentation parameters as used for training, and the result segments are classified using the trained model. The method is tested using two classifiers which are Support Vector Machine (SVM) and Random Forest (RF) with different segmentation parameters. The quantitative results show that RF gives a very useful classification output for such complicated data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.