Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models

TL;DR

This paper presents a method that combines LiDAR point clouds with BIM models using deep learning and graph theory to improve the accuracy and efficiency of urban GeoBIM construction.

Contribution

It introduces a novel integration strategy that enhances urban GeoBIM by combining LiDAR segmentation with BIM model matching, achieving high accuracy in urban scene reconstruction.

Findings

LiDAR segmentation accuracy reaches 90%

Positioning accuracy of BIM models is 0.023 m for poles and 0.156 m for buildings

The method provides a practical solution for rapid urban GeoBIM construction

Abstract

Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.