Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

Abstract

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

Editorial for Special Issue: “Remote Sensing based Building Extraction”

“…Therefore, intelligent and innovative algorithms are in dire need for high success of automatic building extraction and modelling. This Special Issue focuses on the newly-developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D roof modelling.In the Special Issue, the published papers cover a wide range of related topics including building detection [3], boundary extraction [4] and regularization [5], 3D indoor space (room) modelling [6], land cover classification [7], building height model extraction [8], 3D roof modelling [6,9] and change detection [9].In terms of datasets, some of the published works use publicly available benchmark datasets, e.g., ISPRS (International Society for Photogrammetry and Remote Sensing) urban object extraction and modelling datasets [4,5,10]; ISPRS 2D semantic labelling datasets [1]; Inria aerial image labelling benchmark datasets [11][12][13]; and IEEE (Institute of Electrical and Electronics Engineers) DeepGlobe Satellite Challenge datasets [14].The proposed methods fall into two main categories depending the use of the input data sources: Methods based on single source data, and methods that use multi-source data. Methods based on single source data can use point cloud data [9], aerial imagery [4] and digital surface models (DSM) [8].…”

“…The multi-source data-based methods can use the same types of data, e.g., panchromatic band and multispectral imagery [7], optical imagery and light detection and ranging (LiDAR) data [4].Recently, the rapid development of DNNs has been focused in remote sensing, and the networks have achieved remarkable progress in image classification and segmentation tasks [11]. The majority of the articles published in the Special Issue propose classification based on the DNN [1][2][3][4][5][6]8,[11][12][13].There are also a small number of methods based on segmentation [6] and morphological filtering [15].Using aerial LiDAR data, Awrangjeb et al [16] introduce a new 3D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. This method then uses the generated building models to detect 3D changes in buildings.…”

“…Recently, the rapid development of DNNs has been focused in remote sensing, and the networks have achieved remarkable progress in image classification and segmentation tasks [11]. The majority of the articles published in the Special Issue propose classification based on the DNN [1][2][3][4][5][6]8,[11][12][13].…”

“…In the Special Issue, the published papers cover a wide range of related topics including building detection [3], boundary extraction [4] and regularization [5], 3D indoor space (room) modelling [6], land cover classification [7], building height model extraction [8], 3D roof modelling [6,9] and change detection [9].…”

“…There are also a small number of methods based on segmentation [6] and morphological filtering [15].…”