Abstract
Abstract. Visualization of point clouds plays an important role in understanding the context of the digital representation of the built environment. Modern commodity mobile devices (e.g., smartphones and tablets), are capable of capturing representations in the form of 3D point clouds, with their depth-sensing and photogrammetry capabilities. Points clouds enable the encoding of important spatial and physical features of the built environment they represent. However, once captured, point clouds need to be processed before they can be used for further semantic enrichment and decision making. An integrated pipeline for such processes is crucial for use in larger and more complex enterprise systems and data analysis platforms, especially within the realm of Facility Management (FM) and Real Estate 4.0. We present and discuss a prototypical implementation for a service-oriented point cloud processing pipeline. The presented processing features focus on detecting and visualizing spatial deviations between as-is versus as-designed representations. We discuss the design and implementation of these processing features, and present experimental results. The presented approach can be used as a lightweight software component for processing indoor point clouds captured using commodity mobile devices, as well as primary deviation analysis, and also provides a processing link for further semantic enrichment of base-data for Building Information Modeling (BIM) and Digital Twin (DT) applications.
Highlights
1.1 Problem Statement and Research ContributionsWith recent adaptations of Industry 4.0 practices within Architecture, Engineering and Construction (AEC) sectors, the need for routine capture, processing and presentation of digital built environment data is becoming increasingly important
The ability to inspect, monitor, and forecast the current state of the built environment opens new dimensions of stakeholder engagement and enhancement of decision making. These developments concern stakeholders involved with Facility Management (FM) operations, and are becoming part of what is known as Real Estate 4.0 (RE 4.0)
In order to carry out the comparison of spatial deviations between different geometry representations of the built environment, the deviation analysis process requires the compared geometry to be processed in order to contain specific attributes
Summary
With recent adaptations of Industry 4.0 practices within Architecture, Engineering and Construction (AEC) sectors, the need for routine capture, processing and presentation of digital built environment data is becoming increasingly important. In order to carry out the comparison of spatial deviations between different geometry representations of the built environment, the deviation analysis process requires the compared geometry to be processed in order to contain specific attributes. This includes the generation of an additional finite element representation of the as-designed BIM geometry, as well as the post-processing of the compared point cloud geometry. Once post-processed, the point cloud representation of a specific indoor environment can be compared against the finite element version of its as-designed BIM geometry counterpart for spatial deviations (Fig. 1). Implementation for further semantic-enrichment of as-is indoor point cloud representations within the realm of RE 4.0
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