Assessing accuracy and reliability of modern 3D reconstruction and measurement techniques in the evaluation of Byzantine baths: a case study

Assessing accuracy and reliability of modern 3D reconstruction and measurement techniques in the evaluation of Byzantine baths: a case study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing accuracy and reliability of modern 3D reconstruction and measurement techniques in the evaluation of Byzantine baths: a case study Ion-Anastasios Karolos, Konstantinos Bellos, Stylianos Bitharis, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3387735/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In recent years, sophisticated 3D reconstruction and measuring techniques such as Terrestrial Laser Scanning (TLS), LiDAR applications, and Photogrammetry have gained popularity for surveying and mapping of physical environments. Using a case study of UNESCO-recognized Byzantine Bath monument, we examine the accuracy and reliability of different techniques. To assess the accuracy and dependability of modern 3D reconstruction and measurement systems, we created 3D models of the area of interest using a combination of Terrestrial Laser Scanning, LiDAR technology in conjunction with Simultaneous Localization and Mapping (SLAM) systems and algorithms. The geometric features of the 3D models were evaluated by comparing the data generated by these approaches to ground-truth measurements. With errors ranging from 0.01 to 3.2 cm, our results demonstrated that the majority of the available techniques produced highly accurate and trustworthy 3D models of the monument. The TLS and SLAM approaches performed admirably in terms of capturing fine features and the monument’s geometry. Also, the iPhone solution occasionally was more effective at capturing the surface's geometry, texture and colour. Our work demonstrates the efficacy of modern 3D reconstruction and measurement techniques for surveying and mapping environments with precision and dependability. To evaluate our results, we use open-source algorithms and tools in order to quantify the differences from point cloud to ground truth point cloud in many different scenarios (vertical/horizontal sections and floor analysis on the z-axis component). 3D Modelling LiDAR SLAM Terrestrial Laser Scanning Point Cloud Comparison Cultural Heritage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The history of Thessaloniki dates back to the Hellenistic era and has persisted without interruption up to the present day. Its evolution is mainly associated with the city's Byzantine heritage, and the walled city along with its monuments could be characterised as an open-air Byzantine Museum. All of the city's landmarks, including the Byzantine, Post-Byzantine, and Ottoman monuments, have been officially designated as historical landmarks. Additionally, fifteen (15) of the early Christian-Byzantine monuments were added to the UNESCO World Heritage List in 1988. The Byzantine Bath is located in the Koule Kafe district on the edge of Thessaloniki’s Upper Town, near the Byzantine cistern in Olympiados St. and the church of the Archangels, and it's the only public Byzantine bath currently preserved in Thessaloniki. This building has a rectangular configuration and is thought to date back to the 13th century. It is distinguished by the inclusion of an antechamber, warm-room (tepidarium), hot-room (caldarium), and water reservoir, which are essential features of a typical Roman bathhouse, as illustrated in Fig. 1 . 2. Methods and Equipment In this section we briefly describe the selected methods and equipment that are used in the study area. The main scope of our research is to compare five (5) different systems of point cloud generation equipment, namely two Terrestrial Laser Scanners (TLS) and three Simultaneous Localization and Mapping (SLAM) solutions as illustrated in Fig. 2 . We examine the accuracy of 3D point cloud results, among high-level industrial surveying equipment and less expensive LiDAR solutions embedded even on a mobile smartphone - iPhone 12 Pro Max (Luetzenburg et al. 2021 ; Vacca et al. 2023). Our analysis involved conducting multiple scans of the monument from various positions using the Faro Focus 3D LaserScanner. We utilised these measurements as a reference to perform cloud-to-cloud (C2C) algorithms (Kharroubi et al. 2022 ) and assess the distance with multiple techniques (Ingman et al. 2020 ) between the individual metric data obtained from different systems. We used the FARO Focus 3D as the system to provide our reference data due to the high level of retrieved point cloud accuracy. The FARO system can achieve a distance range accuracy of 2 mm, making it a reliable source for obtaining precise measurements. In Table 1 we provide the main specification of the selected instruments that we use in our analysis, as given by each manufacturer (Leica Geosystems 2021 , FARO 2013 , FJDynamics 2023 ). 2.1 Terrestrial Laser Scanners Terrestrial Laser Scanners (TLS) are particularly useful for monument documentation in surveying or forest applications (Gollob et al. 2020 ; Lin et al. 2021 ; Bitharis et al. 2022 ). Monuments are structures that have cultural, historical, or archaeological significance, and accurate geometric documentation of these objects is critical for preservation and research purposes. TLS technology enables surveyors to capture high-resolution three-dimensional data of monuments with great accuracy and detail, providing a permanent record of their current condition. The resulting point cloud data can be used to create detailed 3D models, which can be used for analysis, restoration, and replication purposes. TLS is also helpful in creating detailed documentation of the surrounding environment, which can help provide context for the monument and aid in its interpretation. Overall, the use of TLS in monument documentation enables surveyors to produce accurate and detailed records of monuments (Fig. 3 ). 2.2 Simultaneous Localization and Mapping - SLAM In surveying engineering, SLAM systems can be used to create accurate mapping of human structures and the natural environment. SLAM was developed as a technology for autonomous vehicles that enables them to create maps and localize their positions on those maps (Smith, R. C., & Cheeseman, P. ,1986). Most specifically LiDAR SLAMs can be used to combine data from a laser emitter-receiver with IMU corrections over time. This data can then be used to generate 3D point clouds, which can be further processed to create detailed mapping, floor plans, and elevation models. SLAM systems in surveying are particularly useful in situations where traditional surveying methods are impractical or unsafe, such as in areas with difficult terrain, absence of GNSS signal, or hazardous conditions. SLAM technology is faster and easier to capture data in a fraction of the time required for traditional surveying methods. Also, this kind of technology can operate in quasi real-time, allowing surveyors to quickly collect 3D data of large areas with relatively low cost and high accuracy. Table 1 Specifications of main equipment characteristics. Distance measurement system FARO focus 3D Leica Blk360 iPhone 12 Pro GeoSLAM ZEB-REVO FJD TRION ™ S1 Maximum range (m) 120 45 5 30 120 Max. scan rate (pts/sec) 976.000 680.000 - 43.200 320.000 Resolution settings 1/8 or 1/10 - Indoors and small, outdoor spaces 4 (6/12/25/50 mm @ 10 m) 3 modes (low/medium/high) 30 mm - Laser wavelength (nm) 905 830 940 905 905 3. Post Processing In the post-processing part of the Laser Scanning measurements, we used several software tools (Leica Geosystems 2020 ). The twenty-one (21) point clouds from the Faro Focus 3D were processed with the Faro Scene software application. The scans were aligned and registered using Iterative Closest Point (ICP) algorithm providing a cloud-to-cloud registration to create a unified 3D model of the monument (Besl, P., J., and McKay, N.,D., 1992, Shanoer et al. 2018). The accuracy of the registration is 3.5 mm. For the Leica BLK360 scans the post-processing software that was used is Cyclone Register 360 (Leica Geosystems, 2020 ) using also the ICP algorithm. Twenty (20) scans were performed to capture the environment of interest with over 50% overlap and a registration accuracy of 5 mm. The data from the Geoslam Zeb REVO system were processed from the Geoslam hub post-processing environment and the results are a 3D geometric model of the monument with 5.5 million number of points acquired in a short period of time (5–7 minutes). The accuracy (internal) of the automated registration procedure was not produced by the Geoslam hub application, hence the accuracy (external) will be evaluated through the comparison with the ground-truth data. The data from the FJD TRION™ S1 system were processed in the FJDynamics software and provided a dataset of 3.7 million points in almost the same duration. Finally, the post-processing of the data captured from the LiDAR sensor of the iPhone 12 Pro (Teppati Losè, 2022) is an automated and user-friendly procedure which is performed almost simultaneously with the data captured in an iOS application (i.e. 3D Scan app - link: https://apps.apple.com/us/app/3d-scanner-app/id1419913995 ). In Table 2 , we provide the statistics about the cloud-to-cloud registration procedure between BLK360, GeoSLAM, FJD TRION™ S1 and iPhone 12 Pro with the ground truth dataset - FARO Focus 3D. Must be noted that iPhone 12 statistics are estimated with a theoretical overlap of 40% instead of the 90% that was used for the rest of the datasets. This approach was selected in order to avoid distance range limitations and follow manufacturer recommendations. Table 2 Registration comparison between BLK360, GeoSLAM, FJD TRION™ S1 and iPhone 12 Pro with ground truth dataset - FARO Focus 3D. System Mean Distance (cm) STD (cm) RMS (cm) Leica BLK360 1.0 3.1 6.7 GeoSLAM ZEB-REVO 1.6 2.9 7.6 iPhone 12 3.2 4.1 4.9 FJD TRION™ S1 2.5 4.9 8.9 Table 2 data reveal that the level of accuracy in smartphone devices is lower when compared to professional measuring methods, as expected. 4. Discussion and evaluation of the 3D point cloud accuracy This section is dedicated to analysing the efficacy of various equipment and techniques utilised to capture the environment in specific segments of the monument, with the objective to evaluate their accuracy, as depicted in Fig. 4 . By examining the performance of various equipment and techniques, we can assess their strengths/weaknesses and identify which system is most effective for specific areas or types of measurements. We will evaluate the accuracy of each system based on the quality of the resulting data, taking into account factors such as resolution, point density, and noise levels. Through our analysis, we aim to provide insights into the performance of various systems used for capturing the environment in different parts of the monument, and to offer recommendations on the most suitable systems for particular applications. Specifically, we want to examine the following 3 cases a) the Floor Analysis, b) the Section Analysis and c) Multiscale Model to Model Cloud method. 4.1 Floor analysis A subset of floor points is extracted from each registered point cloud using the same manually-designed geometric extraction shape. In the height direction (z-axis), a comparison is made with reference data. In Table 3 the statistical indices as the mean distance and their standard deviation values of the floor analysis are given. Table 3 Statistics of C2C comparison between BLK360, SLAM, FJD TRION™ S1 and iPhone 12 Pro with ground truth dataset - FARO Focus 3D in the z-axis. System Mean Distance (mm) STD (mm) Leica BLK360 7.8 4.9 GeoSLAM ZEB-REVO 11.3 8.3 FJD TRION™ S1 25 10 iPhone 12 4.7 3.8 According to the results, the point cloud generated by BLK360 is significantly denser (aprox. three times) in comparison with SLAM results (see; Fig. 4 ). Also in terms of accuracy, the BLK Laser Scanner is superior compared to the GeoSLAM and FJD TRION™ S1 point cloud. During our floor examination, we achieved a noteworthy result. By employing the C2C distance algorithm to a limited area of the floor's z-axis, we determined that the point cloud generated by the iPhone 12 was more accurate and closer to the ground truth data than those extracted by the other devices we tested. This outcome was quite interesting. In Fig. 5 we demonstrate three (4) scalar field values of C2C absolute distances (z) diagrams and their histograms for each method. An interesting result is the subplot in Figure -F which demonstrates an impressive amount of points in a relatively close to the ground truth distance. 4.2 Section analysis Following the aforementioned procedure, we also conducted cloud-to-cloud distance measurements for two vertical sections of the monument. This additional analysis is aimed at providing a more comprehensive understanding of the variations in accuracy among the different systems used for capturing the object of our study. Similar to our previous analysis, we compared the output data from each system with the ground-truth dataset to evaluate the accuracy of each system. Based on the results of our analysis, we can assess the strengths and limitations of each system and provide recommendations for selecting the most suitable systems for specific applications. These subsections present the findings of our cloud-to-cloud distance measurements for the two vertical sections of the monument with scalar field values that represent the C2C absolute distances. In Fig. 6 , the four (4) individual methods are illustrated, in the section analysis. As demonstrated in histograms of Fig. 6 , A and B cases are appearing more accurate due to the fact that most of the points are closer to the ground-truth, while C and D are less accurate with a range of 2–4 cm differences. Table 4 Statistical comparison between a) BLK360, b) GeoSLAM c) iPhone 12 Pro and d) FJD TRION™ S1 for sections 1–2. System Mean Distance (mm) STD (mm) Section 1 Leica BLK360 8 11 GeoSLAM ZEB-REVO 12 14 iPhone 12 22 12 FJD TRION™ S1 16 19 Section 2 Leica BLK360 10 8 GeoSLAM ZEB-REVO 13 30 iPhone 12 19 17 FJD TRION™ S1 22 12 4.3 Multiscale model to model cloud comparison Subsequently, we employed the M3C2 (Multiscale Model to Model Cloud Comparison) algorithm to compare the output data from the different systems used for capturing the environment. The M3C2 algorithm is a widely used and well-established method for point cloud comparison, which provides an accurate and robust analysis of differences between two or more-point cloud datasets (Lague et al. 2013 ). The M3C2 algorithm is particularly useful for comparing point cloud data from different systems, as it provides a quantitative evaluation of differences between datasets, rather than relying solely on visual inspection. This allows for more objective and reliable assessments of the performance of different systems and enables a more robust comparison of their accuracy and efficacy. The use of the M3C2 algorithm provides a reliable and accurate method for comparing point cloud data from different systems (Winiwarter, 2021). Its ability to compare datasets at different scales, as well as its robustness to noise and errors, makes it a valuable tool for point cloud analysis in various applications. We used the M3C2 method in two parts of the monument. As a first step of our analysis, we focused on the external facet in the southern part of the monument, as illustrated in Fig. 7 . We selected this part of the monument due to the complex geometrical characteristics of its surface. We should note that in subplot B of Fig. 7 , the points marked in blue and red, respectively, show the points that are further away from the ground-truth. This is thought to be the effect of the laser beam's incidence angle. Next, we performed the same procedure for a thick section of the internal part of the monument. In this step we are able to compare data with the ground truth using the M3C2 algorithm for all available systems (Di Francesco et al. 2020 ). In Fig. 8 the most notable differences are detected in the pointcloud from the iPhone 12 Pro Max. It is important to underline that the points with the red colour in the north-east part of the internal walls are the last part of the monument that was scanned with the iPhone iOS application. This is an expected outcome due to the fact that, in general, the longer the trajectory, the greater the potential of accumulated errors, drift, and uncertainty in the estimated positions and maps. This is because SLAM systems typically rely on integrating measurements from multiple sensors over time to estimate the device position and orientation relative to the environment, and to build a map of the environment. As the device moves over a longer trajectory, errors in the sensor measurements, inaccuracies in the device motion model, and uncertainties in the environment can accumulate, leading to a degradation in the accuracy of the estimated positions and maps. Conclusions Our experiments show that when we used the C2C distance algorithm in the full models the expensive and professional surveying instruments were superior by far to Apple’s 3D point cloud result. When we studied the accuracy of the sections, the result from Apple’s LiDAR is not extremely disappointing compared to the TLS and SLAMs professional equipment but still there is a significant difference. The BLK360,GeoSLAM ZEB Revo and FJD TRION™ S1 have been specifically designed for the purpose of 3D point cloud data capturing, whereas the iPhone 12 is a multipurpose device that has a LiDAR sensor as an additional feature. Therefore, while the iPhone 12 can be used for point cloud 3D mapping, the rest provide superior options due to their specialised features and purpose-built design. The results were similar also when we used the Multiscale Model to Model Cloud Comparison for a part of the internal part of the monument. However, we obtained an intriguing result in the "Floor Analysis". We applied the C2C distance algorithm along the z-axis for a small part of the floor and showed that the point cloud extracted from iPhone 12 was closer to the ground-truth than the others. We assume that this is related to the position of the iPhone in relation to the floor. The LiDAR beam direction of the iPhone was collinear to the direction of the coordinates in study (height-Z) at a small distance (more or less than 1m) while the iPhones’s imaging sensor was parallel to the floor. On the other hand, the position of the scanner and the slam systems were measuring points on the floor from longer distances and the ranging laser beams were emitted in varying angles relative to the floor normal.. There are promising expectations for the upcoming years from two standpoints: the development of devices and software. In terms of device development, Apple's decision to include a LiDAR sensor in their products is significant and showcases the growing interest in this technology for low-cost devices intended for mass consumption, in comparison to more expensive TLS devices. As for software development, the potential market for utilising mobile devices in 3D metric surveying is already emerging, with software companies releasing surveying software that is specifically geared towards leveraging the Apple LiDAR sensor. Declarations Acknowledgements The authors thank the Ephorate of Antiquities of the City of Thessaloniki for their support. Conflict of interest The author has no conflict of interest to declare that are relevant to this article. References Bitharis S, Pikridas C, Tsioukas B, Karolos I-A, Bellos K. 2022. The Contribution of Three-Dimensional Recording of Industrial Infrastructure with Emphasis on Creating a Three-Dimensional Photorealistic Model. Chorografies, Issue 9, ISSN: 1792-3913. Paul J. Besl and Neil D. McKay "Method for registration of 3-D shapes", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57955 Di Francesco P., Bonneau D., Hutchinson J., 2020. The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds FARO, User Manual of Laser Scanner Focus 3D, https://downloads.faro.com/index.php/s/CY5BS9Jd2JEf8YY (2013). FJDynamics, User Manual https://www.fjdynamics.com (2023) Gollob C., Ritter T., Nothdurft A. (2020). Comparison of 3D Point Clouds Obtained by Terrestrial Laser Scanning and Personal Laser Scanning on Forest Inventory Sample Plots Ingman M., Virtanen J., Vaaja M., Hyyppä H., 2020. A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modelling. Kharroubi, A., Poux, F., Ballouch, Z., Hajji, R., Billen, R. (2022). Three Dimensional Change Detection Using Point Clouds: A Review. Lague D., Brodu N., Leroux J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) Leica Geosystems, User Manual of BLK360 3D laser Scanner, https://Leica-Geosystems.Com/Products/Laser-Scanners. (2021). Leica Geosystems, User Manual of Leica Cyclone FIELD 360, Https://Leica-Geosystems.Com/Products/Laser-Scanners/Scanners/Leica-Rtc360. (2020). Lin, G., Giordano, A., Sang, K., Stendardo, L., Yang, X. (2021). Application of Territorial Laser Scanning in 3D Modelling of Traditional Village: A Case Study of Fenghuang Village in China. Luetzenburg G., Kroon A., Bjørk A. (2021). Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences. Revithiadou, F., Raptis, K., 2014. Restoration - Consolidation of the Byzantine Bath in Thessaloniki. Thessaloniki, Ziti Publ. Smith, R. C., & Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The international journal of Robotics Research, 5(4), 56-68. Shanoer M. , Abed F.. (2018). Evaluate 3D laser point clouds registration for cultural heritage documentation. Teppati Losè L., Spreafico A., Chiabrando F., Giulio Tonolo F. (2022). Apple LiDAR Sensor for 3D Surveying: Tests and Results in the Cultural Heritage Domain. Vacca, G. (2023). 3D Survey with Apple LiDAR Sensor–Test and Assessment for Architectural and Cultural Heritage. Winiwarter L., Anders K., Höfle B., 2021. M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 30 Nov, 2023 Reviewers agreed at journal 10 Oct, 2023 Reviewers invited by journal 30 Sep, 2023 Editor assigned by journal 28 Sep, 2023 First submitted to journal 26 Sep, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3387735","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":237091843,"identity":"d7f33e68-ed65-4c45-903c-20a7355e2e79","order_by":0,"name":"Ion-Anastasios Karolos","email":"","orcid":"","institution":"Aristotle University of Thessaloniki Faculty of Engineering: Aristoteleio Panepistemio Thessalonikes Polytechnike Schole","correspondingAuthor":false,"prefix":"","firstName":"Ion-Anastasios","middleName":"","lastName":"Karolos","suffix":""},{"id":237091844,"identity":"6cce0e73-480f-40d1-90a5-265de98301ab","order_by":1,"name":"Konstantinos Bellos","email":"","orcid":"","institution":"Aristotle University of Thessaloniki Faculty of Engineering: Aristoteleio Panepistemio Thessalonikes Polytechnike Schole","correspondingAuthor":false,"prefix":"","firstName":"Konstantinos","middleName":"","lastName":"Bellos","suffix":""},{"id":237091845,"identity":"11c90946-d77b-499e-b486-b1ae8f8717ca","order_by":2,"name":"Stylianos Bitharis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJCCA4wNDAz8cK4EMVoOArVItkF5PMRoYQBpMThGrBb5aWcPHv64wy7f+H7zsQcfGOwS90s3ML+uwKPF4HZewoGDZ5Ittx1jSzecwZCc2CNzgM3yDD4t0jkGBw62MRuYHeMxk+ZhYE7skUhgM2zA57DZYC31BsZtYC31hLUw3AZrOWxgwAbWchikhfkhPi1gv5xtO24gcSwN6BeD48Y9NxLbGPE7LPfwh8q2agP+5sPAEKuolm2fkXz4I16HMfDAWWxAS0E0YxuBqEHRAgHMH/BrGQWjYBSMghEGAChcT4s1r09CAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3799-2149","institution":"Aristotle University of Thessaloniki Faculty of Engineering: Aristoteleio Panepistemio Thessalonikes Polytechnike Schole","correspondingAuthor":true,"prefix":"","firstName":"Stylianos","middleName":"","lastName":"Bitharis","suffix":""},{"id":237091846,"identity":"59a05056-ab86-49c0-b44f-0c109f525988","order_by":3,"name":"Vassilios Tsioukas","email":"","orcid":"","institution":"Aristotle University of Thessaloniki Faculty of Engineering: Aristoteleio Panepistemio Thessalonikes Polytechnike Schole","correspondingAuthor":false,"prefix":"","firstName":"Vassilios","middleName":"","lastName":"Tsioukas","suffix":""},{"id":237091847,"identity":"b7676db8-032e-48b2-a223-7ede8582d1f5","order_by":4,"name":"Christos Pikridas","email":"","orcid":"","institution":"Aristotle University of Thessaloniki Faculty of Engineering: Aristoteleio Panepistemio Thessalonikes Polytechnike Schole","correspondingAuthor":false,"prefix":"","firstName":"Christos","middleName":"","lastName":"Pikridas","suffix":""}],"badges":[],"createdAt":"2023-09-26 08:19:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3387735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3387735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44209357,"identity":"1ce59e5b-78f1-4cc1-9eaf-3522c0ebb5d3","added_by":"auto","created_at":"2023-10-06 20:35:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271652,"visible":true,"origin":"","legend":"\u003cp\u003ePlan of Byzantine Bath (Revithiadou and Raptis, 2014)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/761176cbee2936263cbb8b9b.png"},{"id":44206102,"identity":"764ada65-4957-4392-bcdf-17f581dfd447","added_by":"auto","created_at":"2023-10-06 20:19:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201642,"visible":true,"origin":"","legend":"\u003cp\u003eMeasuring equipment: a) FARO Focus 3D S 120, b) Leica BLK360, c) GeoSLAM REVO, d) iPhone 12 Pro Max and e) FJD TRION™ S1\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/bc55909eaab2fd234b85c63e.png"},{"id":44207530,"identity":"19c36fe0-69b7-4886-ae09-9cac51443cc5","added_by":"auto","created_at":"2023-10-06 20:27:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":576114,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed digitisation of facades and their connection with the top-view drafting design. (source: E.-M. Karakasi, Dipl.Thesis, “3D geometric documentation of Profitis Ilias Chapel in Tyrnavos, Greece”, 2012)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/3b0b37848849881bada2ee4b.png"},{"id":44206105,"identity":"df33acdf-bc57-492a-8c5b-faee3daaf10c","added_by":"auto","created_at":"2023-10-06 20:19:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":459766,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of segmented positions.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/0fd74853dab515c80f337db4.png"},{"id":44206108,"identity":"515592d8-1bdb-4fc1-88f6-6e29da0b7a3e","added_by":"auto","created_at":"2023-10-06 20:19:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2412729,"visible":true,"origin":"","legend":"\u003cp\u003eFloor analysis comparison (3 x 3 m) between a-b) BLK360 c-d) GeoSLAM e-f) iPhone point clouds g-h) FJD TRION™ S1 and their statistics.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/ca182e0b27426e431bd11f38.png"},{"id":44206104,"identity":"89c6286e-c780-4dec-ac0a-016b346933b8","added_by":"auto","created_at":"2023-10-06 20:19:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":349064,"visible":true,"origin":"","legend":"\u003cp\u003eSection comparison between a) BLK360, b) GeoSLAM and c) iPhone 12 Pro d) FJD TRION™ S1 point clouds for sections 1-2.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/2c0d60f9d4aaae98cc01c316.png"},{"id":44207531,"identity":"0def597a-3852-4c32-9af8-ba549901409e","added_by":"auto","created_at":"2023-10-06 20:27:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1117334,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between a) BLK360 and b) GeoSLAM c) FJD TRION™ S1 using M3C2 algorithm\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/b7ee42b94f66e3a6c2dc5039.png"},{"id":44206106,"identity":"522e1051-d1c2-4e9b-aa83-53c493ae89cf","added_by":"auto","created_at":"2023-10-06 20:19:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":471446,"visible":true,"origin":"","legend":"\u003cp\u003eM3C2 distances of a thick section of the internal part of the monument between a) BLK360 and b) GeoSLAM and c) iPhone 12 Pro d) FJD TRION™ S1 .\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/6634a4ba3bf9d2888196c1f7.png"},{"id":44211176,"identity":"d8242c14-8db4-431a-8cfa-731f36f46003","added_by":"auto","created_at":"2023-10-06 20:43:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4697701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3387735/v1/e9926615-bec5-4b47-82d6-7d7be3ba9835.pdf"}],"financialInterests":"","formattedTitle":"Assessing accuracy and reliability of modern 3D reconstruction and measurement techniques in the evaluation of Byzantine baths: a case study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe history of Thessaloniki dates back to the Hellenistic era and has persisted without interruption up to the present day. Its evolution is mainly associated with the city's Byzantine heritage, and the walled city along with its monuments could be characterised as an open-air Byzantine Museum. All of the city's landmarks, including the Byzantine, Post-Byzantine, and Ottoman monuments, have been officially designated as historical landmarks. Additionally, fifteen (15) of the early Christian-Byzantine monuments were added to the UNESCO World Heritage List in 1988.\u003c/p\u003e \u003cp\u003eThe Byzantine Bath is located in the Koule Kafe district on the edge of Thessaloniki\u0026rsquo;s Upper Town, near the Byzantine cistern in Olympiados St. and the church of the Archangels, and it's the only public Byzantine bath currently preserved in Thessaloniki. This building has a rectangular configuration and is thought to date back to the 13th century. It is distinguished by the inclusion of an antechamber, warm-room (tepidarium), hot-room (caldarium), and water reservoir, which are essential features of a typical Roman bathhouse, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e "},{"header":"2. Methods and Equipment","content":"\u003cp\u003eIn this section we briefly describe the selected methods and equipment that are used in the study area. The main scope of our research is to compare five (5) different systems of point cloud generation equipment, namely two Terrestrial Laser Scanners (TLS) and three Simultaneous Localization and Mapping (SLAM) solutions as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. We examine the accuracy of 3D point cloud results, among high-level industrial surveying equipment and less expensive LiDAR solutions embedded even on a mobile smartphone - iPhone 12 Pro Max (Luetzenburg et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vacca et al. 2023).\u003c/p\u003e\n\u003cp\u003eOur analysis involved conducting multiple scans of the monument from various positions using the Faro Focus 3D LaserScanner. We utilised these measurements as a reference to perform cloud-to-cloud (C2C) algorithms (Kharroubi et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and assess the distance with multiple techniques (Ingman et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) between the individual metric data obtained from different systems. We used the FARO Focus 3D as the system to provide our reference data due to the high level of retrieved point cloud accuracy. The FARO system can achieve a distance range accuracy of 2 mm, making it a reliable source for obtaining precise measurements. In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e we provide the main specification of the selected instruments that we use in our analysis, as given by each manufacturer (Leica Geosystems \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, FARO \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e, FJDynamics \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Terrestrial Laser Scanners\u003c/h2\u003e\n\u003cp\u003eTerrestrial Laser Scanners (TLS) are particularly useful for monument documentation in surveying or forest applications (Gollob et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lin et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bitharis et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Monuments are structures that have cultural, historical, or archaeological significance, and accurate geometric documentation of these objects is critical for preservation and research purposes. TLS technology enables surveyors to capture high-resolution three-dimensional data of monuments with great accuracy and detail, providing a permanent record of their current condition. The resulting point cloud data can be used to create detailed 3D models, which can be used for analysis, restoration, and replication purposes. TLS is also helpful in creating detailed documentation of the surrounding environment, which can help provide context for the monument and aid in its interpretation. Overall, the use of TLS in monument documentation enables surveyors to produce accurate and detailed records of monuments (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Simultaneous Localization and Mapping - SLAM\u003c/h2\u003e\n\u003cp\u003eIn surveying engineering, SLAM systems can be used to create accurate mapping of human structures and the natural environment. SLAM was developed as a technology for autonomous vehicles that enables them to create maps and localize their positions on those maps (Smith, R. C., \u0026amp; Cheeseman, P. ,1986). Most specifically LiDAR SLAMs can be used to combine data from a laser emitter-receiver with IMU corrections over time. This data can then be used to generate 3D point clouds, which can be further processed to create detailed mapping, floor plans, and elevation models. SLAM systems in surveying are particularly useful in situations where traditional surveying methods are impractical or unsafe, such as in areas with difficult terrain, absence of GNSS signal, or hazardous conditions. SLAM technology is faster and easier to capture data in a fraction of the time required for traditional surveying methods. Also, this kind of technology can operate in quasi real-time, allowing surveyors to quickly collect 3D data of large areas with relatively low cost and high accuracy.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSpecifications of main equipment characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDistance measurement system\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFARO\u003c/p\u003e\n\u003cp\u003efocus 3D\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLeica Blk360\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eiPhone\u003c/p\u003e\n\u003cp\u003e12 Pro\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGeoSLAM\u003c/p\u003e\n\u003cp\u003eZEB-REVO\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFJD TRION\u003cem\u003e\u0026trade;\u003c/em\u003e S1\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum range (m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax. scan rate (pts/sec)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e976.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e680.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e320.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResolution settings\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1/8 or 1/10 - Indoors and small, outdoor spaces\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (6/12/25/50 mm @ 10 m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 modes (low/medium/high)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLaser wavelength (nm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e905\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Post Processing","content":"\u003cp\u003eIn the post-processing part of the Laser Scanning measurements, we used several software tools (Leica Geosystems \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The twenty-one (21) point clouds from the Faro Focus 3D were processed with the Faro Scene software application. The scans were aligned and registered using Iterative Closest Point (ICP) algorithm providing a cloud-to-cloud registration to create a unified 3D model of the monument (Besl, P., J., and McKay, N.,D., 1992, Shanoer et al. 2018). The accuracy of the registration is 3.5 mm. For the Leica BLK360 scans the post-processing software that was used is Cyclone Register 360 (Leica Geosystems, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) using also the ICP algorithm. Twenty (20) scans were performed to capture the environment of interest with over 50% overlap and a registration accuracy of 5 mm.\u003c/p\u003e\n\u003cp\u003eThe data from the Geoslam Zeb REVO system were processed from the Geoslam hub post-processing environment and the results are a 3D geometric model of the monument with 5.5\u0026nbsp;million number of points acquired in a short period of time (5\u0026ndash;7 minutes). The accuracy (internal) of the automated registration procedure was not produced by the Geoslam hub application, hence the accuracy (external) will be evaluated through the comparison with the ground-truth data. The data from the FJD TRION\u0026trade; S1 system were processed in the FJDynamics software and provided a dataset of 3.7\u0026nbsp;million points in almost the same duration.\u003c/p\u003e\n\u003cp\u003eFinally, the post-processing of the data captured from the LiDAR sensor of the iPhone 12 Pro (Teppati Los\u0026egrave;, 2022) is an automated and user-friendly procedure which is performed almost simultaneously with the data captured in an iOS application (i.e. 3D Scan app - link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.apple.com/us/app/3d-scanner-app/id1419913995\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, we provide the statistics about the cloud-to-cloud registration procedure between BLK360, GeoSLAM, FJD TRION\u0026trade; S1 and iPhone 12 Pro with the ground truth dataset - FARO Focus 3D. Must be noted that iPhone 12 statistics are estimated with a theoretical overlap of 40% instead of the 90% that was used for the rest of the datasets. This approach was selected in order to avoid distance range limitations and follow manufacturer recommendations.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRegistration comparison between BLK360, GeoSLAM, FJD TRION\u0026trade; S1 and iPhone 12 Pro with ground truth dataset - FARO Focus 3D.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSystem\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean Distance (cm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSTD (cm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRMS (cm)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeica BLK360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeoSLAM\u003c/p\u003e\n\u003cp\u003eZEB-REVO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eiPhone 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFJD TRION\u0026trade; S1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e data reveal that the level of accuracy in smartphone devices is lower when compared to professional measuring methods, as expected.\u003c/p\u003e"},{"header":"4. Discussion and evaluation of the 3D point cloud accuracy","content":"\u003cp\u003eThis section is dedicated to analysing the efficacy of various equipment and techniques utilised to capture the environment in specific segments of the monument, with the objective to evaluate their accuracy, as depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eBy examining the performance of various equipment and techniques, we can assess their strengths/weaknesses and identify which system is most effective for specific areas or types of measurements. We will evaluate the accuracy of each system based on the quality of the resulting data, taking into account factors such as resolution, point density, and noise levels. Through our analysis, we aim to provide insights into the performance of various systems used for capturing the environment in different parts of the monument, and to offer recommendations on the most suitable systems for particular applications.\u003c/p\u003e\n\u003cp\u003eSpecifically, we want to examine the following 3 cases a) the Floor Analysis, b) the Section Analysis and c) Multiscale Model to Model Cloud method.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Floor analysis\u003c/h2\u003e\n \u003cp\u003eA subset of floor points is extracted from each registered point cloud using the same manually-designed geometric extraction shape. In the height direction (z-axis), a comparison is made with reference data. In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e the statistical indices as the mean distance and their standard deviation values of the floor analysis are given.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics of C2C comparison between BLK360, SLAM, FJD TRION\u0026trade; S1 and iPhone 12 Pro with ground truth dataset - FARO Focus 3D in the z-axis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Distance (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSTD (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeica BLK360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeoSLAM ZEB-REVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFJD TRION\u0026trade; S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiPhone 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to the results, the point cloud generated by BLK360 is significantly denser (aprox. three times) in comparison with SLAM results (see; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Also in terms of accuracy, the BLK Laser Scanner is superior compared to the GeoSLAM and FJD TRION\u0026trade; S1 point cloud.\u003c/p\u003e\n \u003cp\u003eDuring our floor examination, we achieved a noteworthy result. By employing the C2C distance algorithm to a limited area of the floor\u0026apos;s z-axis, we determined that the point cloud generated by the iPhone 12 was more accurate and closer to the ground truth data than those extracted by the other devices we tested. This outcome was quite interesting. In Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e we demonstrate three (4) scalar field values of C2C absolute distances (z) diagrams and their histograms for each method. An interesting result is the subplot in Figure -F which demonstrates an impressive amount of points in a relatively close to the ground truth distance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Section analysis\u003c/h2\u003e\n \u003cp\u003eFollowing the aforementioned procedure, we also conducted cloud-to-cloud distance measurements for two vertical sections of the monument. This additional analysis is aimed at providing a more comprehensive understanding of the variations in accuracy among the different systems used for capturing the object of our study. Similar to our previous analysis, we compared the output data from each system with the ground-truth dataset to evaluate the accuracy of each system.\u003c/p\u003e\n \u003cp\u003eBased on the results of our analysis, we can assess the strengths and limitations of each system and provide recommendations for selecting the most suitable systems for specific applications. These subsections present the findings of our cloud-to-cloud distance measurements for the two vertical sections of the monument with scalar field values that represent the C2C absolute distances. In Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the four (4) individual methods are illustrated, in the section analysis. As demonstrated in histograms of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, A and B cases are appearing more accurate due to the fact that most of the points are closer to the ground-truth, while C and D are less accurate with a range of 2\u0026ndash;4 cm differences.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical comparison between a) BLK360, b) GeoSLAM c) iPhone 12 Pro and d) FJD TRION\u0026trade; S1 for sections 1\u0026ndash;2.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Distance (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSTD (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eSection 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeica BLK360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeoSLAM ZEB-REVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiPhone 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFJD TRION\u0026trade; S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eSection 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeica BLK360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeoSLAM ZEB-REVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiPhone 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFJD TRION\u0026trade; S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Multiscale model to model cloud comparison\u003c/h2\u003e\n \u003cp\u003eSubsequently, we employed the M3C2 (Multiscale Model to Model Cloud Comparison) algorithm to compare the output data from the different systems used for capturing the environment. The M3C2 algorithm is a widely used and well-established method for point cloud comparison, which provides an accurate and robust analysis of differences between two or more-point cloud datasets (Lague et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe M3C2 algorithm is particularly useful for comparing point cloud data from different systems, as it provides a quantitative evaluation of differences between datasets, rather than relying solely on visual inspection. This allows for more objective and reliable assessments of the performance of different systems and enables a more robust comparison of their accuracy and efficacy. The use of the M3C2 algorithm provides a reliable and accurate method for comparing point cloud data from different systems (Winiwarter, 2021). Its ability to compare datasets at different scales, as well as its robustness to noise and errors, makes it a valuable tool for point cloud analysis in various applications. We used the M3C2 method in two parts of the monument. As a first step of our analysis, we focused on the external facet in the southern part of the monument, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. We selected this part of the monument due to the complex geometrical characteristics of its surface. We should note that in subplot B of Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the points marked in blue and red, respectively, show the points that are further away from the ground-truth. This is thought to be the effect of the laser beam\u0026apos;s incidence angle.\u003c/p\u003e\n \u003cp\u003eNext, we performed the same procedure for a thick section of the internal part of the monument. In this step we are able to compare data with the ground truth using the M3C2 algorithm for all available systems (Di Francesco et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e the most notable differences are detected in the pointcloud from the iPhone 12 Pro Max. It is important to underline that the points with the red colour in the north-east part of the internal walls are the last part of the monument that was scanned with the iPhone iOS application. This is an expected outcome due to the fact that, in general, the longer the trajectory, the greater the potential of accumulated errors, drift, and uncertainty in the estimated positions and maps. This is because SLAM systems typically rely on integrating measurements from multiple sensors over time to estimate the device position and orientation relative to the environment, and to build a map of the environment. As the device moves over a longer trajectory, errors in the sensor measurements, inaccuracies in the device motion model, and uncertainties in the environment can accumulate, leading to a degradation in the accuracy of the estimated positions and maps.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur experiments show that when we used the C2C distance algorithm in the full models the expensive and professional surveying instruments were superior by far to Apple\u0026rsquo;s 3D point cloud result. When we studied the accuracy of the sections, the result from Apple\u0026rsquo;s LiDAR is not extremely disappointing compared to the TLS and SLAMs professional equipment but still there is a significant difference. The BLK360,GeoSLAM ZEB Revo and FJD TRION\u0026trade; S1 have been specifically designed for the purpose of 3D point cloud data capturing, whereas the iPhone 12 is a multipurpose device that has a LiDAR sensor as an additional feature. Therefore, while the iPhone 12 can be used for point cloud 3D mapping, the rest provide superior options due to their specialised features and purpose-built design. The results were similar also when we used the Multiscale Model to Model Cloud Comparison for a part of the internal part of the monument.\u003c/p\u003e\n\u003cp\u003eHowever, we obtained an intriguing result in the \"Floor Analysis\". We applied the C2C distance algorithm along the z-axis for a small part of the floor and showed that the point cloud extracted from iPhone 12 was closer to the ground-truth than the others. We assume that this is related to the position of the iPhone in relation to the floor. The LiDAR beam direction of the iPhone was collinear to the direction of the coordinates in study (height-Z) at a small distance (more or less than 1m) while the iPhones\u0026rsquo;s imaging sensor was parallel to the floor. On the other hand, the position of the scanner and the slam systems were measuring points on the floor from longer distances and the ranging laser beams were emitted in varying angles relative to the floor normal.. There are promising expectations for the upcoming years from two standpoints: the development of devices and software. In terms of device development, Apple's decision to include a LiDAR sensor in their products is significant and showcases the growing interest in this technology for low-cost devices intended for mass consumption, in comparison to more expensive TLS devices. As for software development, the potential market for utilising mobile devices in 3D metric surveying is already emerging, with software companies releasing surveying software that is specifically geared towards leveraging the Apple LiDAR sensor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank the Ephorate of Antiquities of the City of Thessaloniki for their support.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe author has no conflict of interest to declare that are relevant to this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBitharis S, Pikridas C, Tsioukas B, Karolos I-A, Bellos K. 2022. The Contribution of Three-Dimensional Recording of Industrial Infrastructure with Emphasis on Creating a Three-Dimensional Photorealistic Model. Chorografies, Issue 9, ISSN: 1792-3913.\u003c/li\u003e\n\u003cli\u003ePaul J. Besl and Neil D. McKay \u0026quot;Method for registration of 3-D shapes\u0026quot;, Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57955\u003c/li\u003e\n\u003cli\u003eDi Francesco P., Bonneau D., Hutchinson J., 2020. The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds\u003c/li\u003e\n\u003cli\u003eFARO, User Manual of Laser Scanner Focus 3D, https://downloads.faro.com/index.php/s/CY5BS9Jd2JEf8YY (2013).\u003c/li\u003e\n\u003cli\u003eFJDynamics, User Manual https://www.fjdynamics.com (2023)\u003c/li\u003e\n\u003cli\u003eGollob C., Ritter T., Nothdurft A. (2020). Comparison of 3D Point Clouds Obtained by Terrestrial Laser Scanning and Personal Laser Scanning on Forest Inventory Sample Plots\u003c/li\u003e\n\u003cli\u003eIngman M., Virtanen J., Vaaja M., Hyypp\u0026auml; H., 2020. A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modelling.\u003c/li\u003e\n\u003cli\u003eKharroubi, A., Poux, F., Ballouch, Z., Hajji, R., Billen, R. (2022). Three Dimensional Change Detection Using Point Clouds: A Review. \u003c/li\u003e\n\u003cli\u003eLague D., Brodu N., Leroux J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)\u003c/li\u003e\n\u003cli\u003eLeica Geosystems, User Manual of BLK360 3D laser Scanner, https://Leica-Geosystems.Com/Products/Laser-Scanners. (2021).\u003c/li\u003e\n\u003cli\u003eLeica Geosystems, User Manual of Leica Cyclone FIELD 360, Https://Leica-Geosystems.Com/Products/Laser-Scanners/Scanners/Leica-Rtc360. (2020).\u003c/li\u003e\n\u003cli\u003eLin, G., Giordano, A., Sang, K., Stendardo, L., Yang, X. (2021). Application of Territorial Laser Scanning in 3D Modelling of Traditional Village: A Case Study of Fenghuang Village in China. \u003c/li\u003e\n\u003cli\u003eLuetzenburg G., Kroon A., Bj\u0026oslash;rk A. (2021). Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences. \u003c/li\u003e\n\u003cli\u003eRevithiadou, F., Raptis, K., 2014. Restoration - Consolidation of the Byzantine Bath in Thessaloniki. Thessaloniki, Ziti Publ.\u003c/li\u003e\n\u003cli\u003eSmith, R. C., \u0026amp; Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The international journal of Robotics Research, 5(4), 56-68.\u003c/li\u003e\n\u003cli\u003eShanoer M. , Abed F.. (2018). Evaluate 3D laser point clouds registration for cultural heritage documentation.\u003c/li\u003e\n\u003cli\u003eTeppati Los\u0026egrave; L., Spreafico A., Chiabrando F., Giulio Tonolo F. (2022). Apple LiDAR Sensor for 3D Surveying: Tests and Results in the Cultural Heritage Domain.\u003c/li\u003e\n\u003cli\u003eVacca, G. (2023). 3D Survey with Apple LiDAR Sensor\u0026ndash;Test and Assessment for Architectural and Cultural Heritage. \u003c/li\u003e\n\u003cli\u003eWiniwarter L., Anders K., H\u0026ouml;fle B., 2021. M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"3D Modelling, LiDAR, SLAM, Terrestrial Laser Scanning, Point Cloud Comparison, Cultural Heritage","lastPublishedDoi":"10.21203/rs.3.rs-3387735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3387735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, sophisticated 3D reconstruction and measuring techniques such as Terrestrial Laser Scanning (TLS), LiDAR applications, and Photogrammetry have gained popularity for surveying and mapping of physical environments. Using a case study of UNESCO-recognized Byzantine Bath monument, we examine the accuracy and reliability of different techniques. To assess the accuracy and dependability of modern 3D reconstruction and measurement systems, we created 3D models of the area of interest using a combination of Terrestrial Laser Scanning, LiDAR technology in conjunction with Simultaneous Localization and Mapping (SLAM) systems and algorithms. The geometric features of the 3D models were evaluated by comparing the data generated by these approaches to ground-truth measurements. With errors ranging from 0.01 to 3.2 cm, our results demonstrated that the majority of the available techniques produced highly accurate and trustworthy 3D models of the monument. The TLS and SLAM approaches performed admirably in terms of capturing fine features and the monument\u0026rsquo;s geometry. Also, the iPhone solution occasionally was more effective at capturing the surface's geometry, texture and colour. Our work demonstrates the efficacy of modern 3D reconstruction and measurement techniques for surveying and mapping environments with precision and dependability. To evaluate our results, we use open-source algorithms and tools in order to quantify the differences from point cloud to ground truth point cloud in many different scenarios (vertical/horizontal sections and floor analysis on the z-axis component).\u003c/p\u003e","manuscriptTitle":"Assessing accuracy and reliability of modern 3D reconstruction and measurement techniques in the evaluation of Byzantine baths: a case study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-06 20:19:37","doi":"10.21203/rs.3.rs-3387735/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2023-12-01T04:39:43+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-10-10T04:59:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-30T16:14:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-28T06:34:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Geomatics","date":"2023-09-27T03:09:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c46a4dd5-d7b2-4779-a785-5888493db560","owner":[],"postedDate":"October 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T11:36:27+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-06 20:19:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3387735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3387735","identity":"rs-3387735","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}