3D Point Cloud Data Enabled Facility Management: A Critical Review

Although the value of 3D point cloud data (PCD) has been increasingly recognized by the architectural, engineering, construction and facility operations (AECO) sectors, there is much less actual application of PCD in facility management (FM) than other stages. In order to facilitate the exploration of using PCD for FM, this study aims to summarize existing research effort and identify the gaps based on a systematic review of previous studies touching upon the PCD-enabled FM. This review was guided by a conceptual model that consists of four key components associated with PCD application process, including target objects, PCD sensing, model output and applications. 47 papers published in 21 academic journals were collected for the analysis. It was found that Light Detection and Ranging (LiDAR), photogrammetry, and Synthetic Aperture Radar (SAR) were the three mostly used technologies for collecting the PCD. The raw signals, such as fragments of point cloud and photos, collected by these technologies need to be pre-processed for generating the PCD, and segmentation and meshing are two general aspects of PCD post-processing to create models. It was also found that most studies focused on geometric properties, data processing, feature extraction, object recognition, and model generation, seldom would they dig deeper for decision-making support of FM applications. Based on the results, three major gaps of PCD-enabled FM were concluded, including (1) overlooking the valuable non-geometric properties (e.g. specifications of materials, relations between objects); (2) less focusing on providing decision support functions; and (3) hovering at data level rather than information level. Eleven possible research directions including semantics enrichment, real-time model generation, longitudinal analysis, and smart living applications of PCD-enabled FM were thus suggested for future research.