Evaluation of 3D Reconstruction Methods from Terrestrial LiDAR Point Cloud Data to Create Digital Twins of Civil Infrastructure Projects
Publication: International Conference on Transportation and Development 2022
ABSTRACT
This paper presents an assessment of existing approaches to reconstruct objects via point cloud data with a focus on infrastructure. Light detection and ranging (LiDAR) is a popular remote-sensing method used to collect data for high-resolution models which produces large point cloud data sets. The objective of this research is to facilitate the creation of a digital twin (DT) of a building using point cloud data via automated 3D-model generation and plane classification. This capacity is critical if civil engineering is going to adopt DT at scale for the built environment—much of which is not currently documented in digital form. The 3D reconstruction of large point cloud data sets of infrastructure is an open challenge, in part because of the size of the scan required to create a DT model. An efficient, automated reconstruction method is needed to process LiDAR data. Three reconstruction methods are implemented and qualitatively compared: (1) scan-to-building information model (BIM) approach, (2) oriented point sampling (OPS) algorithm with an emphasis in planar recognition, and (3) RANdom SAmple Consensus (RANSAC) algorithm via the open-source software, CloudCompare. The RANSAC and OPS methods both reduce the size of the point cloud data to improve the computational speed as compared to other traditional methods. A portion of a university campus was chosen as a case study. Ongoing research is focused on creating a DT of a university campus, and this work specifically supports the creation of virtual replicas of large campus infrastructure (i.e., buildings) as one component of a DT. The focus is on the scalability of large data sets from infrastructure projects to compare the accuracy, speed, and computation requirements of each method.
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International Conference on Transportation and Development 2022
Pages: 81 - 92
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Published online: Aug 31, 2022
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