Indoor Mapping Using Machine Learning Based Classification of 3D Point Clouds

Abstract

Today, indoor maps remain a valuable source of spatial information for various indoor environments. Classifying 3D point clouds from indoor environments is crucial for indoor mapping. In this study, indoor point clouds from the S3DIS dataset were classified using Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Attentive Interpretable Tabular Learning (TabNet). The classification performances, based on overall accuracy and F1 scores, can be ranked as RF, MLP, XGBoost, and TabNet. It has been determined that machine learning algorithms can be used to classify indoor point clouds for indoor mapping.