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

Cui Cui,Li Li,Dong Dong

doi:10.3390/rs11192262

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.

Highlights

Three-dimensional (3D) reconstruction of the indoor environment has received significant attention due to the development of smart cities
The room segmentation results of the first and second floors showed that the unstructured point clouds were correctly partitioned based on the multi-label graph cuts
The current bottleneck in 3D indoor reconstruction is the low level of automation and accuracy in the reconstruction of the complex indoor environment

Summary

Introduction

Three-dimensional (3D) reconstruction of the indoor environment has received significant attention due to the development of smart cities. 3D Indoor models are often generated manually by creating geometric representations using point cloud data and commercial software. This often requires significant investment in time and in training personnel [9]. MLS systems can obtain point clouds by moving from different spaces and measure from different locations. The easy-to-use and affordable indoor MLS systems are mostly used for data acquisition of large indoor scenes [11]. While MLS ensures good coverage for indoor environment mapping, the data can be affected by a number of factors (e.g., moving objects, multiple reflections, and dynamic occlusions) resulting in quality losses, which present serious challenges in model reconstruction

Results

Discussion

Conclusion