As-Textured As-Built BIM Using Sensor Fusion, Zee Ain Historical Village as a Case Study

Abstract

The work described in the paper emphasizes the importance of integrating imagery and laser scanner techniques (TLS) to optimize the geometry and visual quality of Heritage BIM. The fusion-based workflow was approached during the recording of Zee Ain Historical Village in Saudi Arabia. The village is a unique example of traditional human settlements, and represents a complex natural and cultural heritage site. The proposed workflow divides data integration into two levels. At the basic level, UAV photogrammetry with enhanced mobility and visibility is used to map the ragged terrain and supplement TLS point data in upper and unaccusable building zones where shadow data originated. The merging of point clouds ensures that the building’s overall geometry is correctly rebuilt and that data interpretation is improved during HBIM digitization. In addition to the correct geometry, texture mapping is particularly important in the area of cultural heritage. Constructing a realistic texture remains a challenge in HBIM; because the standard texture and materials provided in BIM libraries do not allow for reliable representation of heritage structures, mapping and sharing information are not always truthful. Thereby, at the second level, the workflow proposed true orthophoto texturing method for HBIM models by combining close-range imagery and laser data. True orthophotos have uniform scale that depicts all objects in their respective planimetric positions, providing reliable and realistic mapping. The process begins with the development of a Digital Surface Model (DSM) by sampling TLS 3D points in a regular grid, with each cell uniquely associated with a model point. Then each DSM cell is projected in the corresponding perspective imagery in order to map the relevant spectral information. The methods allow for flexible data fusion and image capture using either a TLS-installed camera or a separate camera at the optimal time and viewpoint for radiometric data. The developed workflows demonstrated adequate results in terms of complete and realistic textured HBIM, allowing for a better understanding of the complex heritage structures.

Static Terrestrial Laser Scanning (TLS) for Heritage Building Information Modeling (HBIM): A Systematic Review

“…A new laser scanner can easily cost USD tens of thousands [46]. It requires specialty software, substantial hardware, and highly trained professionals to process and handle the scan data [41,48]. A TLS survey can also be time-consuming [49], especially when it is used to capture large and complex heritage sites and is sensitive to the environment such as lighting conditions, dust, fog, rain, etc.…”

“…Combine multiple point cloud datasets created from different approaches (e.g., TLS and aerial photogrammetry) into one single point cloud. [12,44,46,48,[52][53][54]57] Number of software applications required to process the scan data [22,50,57,[62][63][64] Processing TLS scans is time-consuming. [22,65] Incorporation of TLS scans from two separate campaigns that were 10 years apart.…”

“…Due to the complexity of the geometry of the built heritage and the limitations of the laser scanning technology, TLS is not a sufficient system for complete data acquisition. Therefore, it usually takes other technological tools and techniques to capture additional data about the heritage structure to supplement the TLS survey or to help improve the accuracy and coverage of the TLS survey [48,68,69]. These tools and techniques can be classified into the following groups:…”

“…TLS is a more expensive technology in comparison with photogrammetric surveys, and higher knowledge is required to perform laser scans than to take photographs for 3D photoreconstructions [68]. On the other hand, photogrammetry is more cost-effective, more flexible, faster, and capable of collecting accurate and quality data for complex objects [48]. The benefit of integrating these two technologies is to take advantage of the TLS's capability to acquire an accurate and dense point cloud and the flexibility of photogrammetry to operate even in exceptional conditions.…”

“…The fusion of two individual point clouds can be conducted using Iterative Closest Point (ICP), an algorithm that minimizes the difference between two point clouds, transforms the source points, and reassociates them [81]. One of the most challenging aspects of point cloud integration is aligning all point clouds and converting them into the same coordinate system [48]. A common approach that was identified in the literature to overcome this hurdle was using the established GCPs as a shared reference in all point cloud data for alignment and scaling [22].…”