Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

Domenica Costantino; Gabriele Vozza; Massimiliano Pepe; Vincenzo Saverio Alfio

doi:10.3390/asi5040063

Costantino, Domenica;Vozza, Gabriele;Pepe, Massimiliano;Alfio, Vincenzo Saverio

2022-06-29 00:00:00

Article Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges Domenica Costantino * , Gabriele Vozza, Massimiliano Pepe and Vincenzo Saverio Alﬁo Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica, Polytechnic of Bari, Via E. Orabona 4, 70125 Bari, Italy; gabriele.vozza@poliba.it (G.V.); massimiliano.pepe@poliba.it (M.P.); vincenzosaverio.alﬁo@poliba.it (V.S.A.) * Correspondence: domenica.costantino@poliba.it Abstract: The aim of the research was to evaluate the performance of smartphone depth sensors (Time of Flight Camera(ToF) and Light Detection and Ranging (LiDAR)) from Android (Huawei P30 Pro) and iOS (iPhone 12 Pro and iPAD 2021 Pro) devices in order to build a 3D point cloud. In particular, the smartphones were tested in several case studies involving the scanning of several objects: 10 building material samples, a statue, an interior room environment and the remains of a Doric column in a major archaeological site. The quality of the point clouds was evaluated through visual analysis and using three eigenfeatures: surface variation, planarity and omnivariance. Based on this approach, some issues with the point clouds generated by smartphones were highlighted, such as surface splitting, loss of planarity and inertial navigation system drift problems. In addition, it can ﬁnally be deduced that, in the absence of scanning problems, the accuracies achievable from this type of scanning are ~1–3 cm. Therefore, this research intends to describe a method of quantifying anomalies occurring in smartphone scans and, more generally, to verify the quality of the point cloud Citation: Costantino, D.; Vozza, G.; obtained with these devices. Pepe, M.; Alﬁo, V.S. Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Keywords: smartphone; LiDAR; point cloud analysis; ToF; Android; iOS Scenarios: Evaluation Methods, Performance and Challenges. Appl. Syst. Innov. 2022, 5, 63. https:// doi.org/10.3390/asi5040063 1. Introduction Academic Editors: Teen-Hang Meen In recent years, smartphones equipped with depth sensors were released onto the and Chun-Yen Chang consumer market. These sensors were advertised as “LiDAR scanners” for iOS devices and “time-of-ﬂight depth cameras” (ToF cameras) for Android. The sensors were originally Received: 9 June 2022 used to improve the quality of photos (e.g., improved camera focus, bokeh effect, etc.) Accepted: 27 June 2022 and to enable augmented reality applications, but they proved to be suitable for scientiﬁc Published: 29 June 2022 purposes [1–3]. Publisher’s Note: MDPI stays neutral Historically, Android smartphones integrated depth sensors and augmented reality with regard to jurisdictional claims in applications ﬁrst. The ﬁrst smartphone equipped with ToF camera and augmented reality published maps and institutional afﬁl- features released in the consumer market was the Lenovo Phab 2 Pro in 2016; subsequently, iations. other devices were released such as the ASUS Zenfone AR in 2017, Oppo RX17 Pro, Honor View 20 in 2018, etc. To support augmented reality (AR) on Android smartphones, Google developed the Tango Project. The Tango technology was based on three fundamental parts: depth sensing, mapping motion and area learning. The ﬁrst part used an RGB-D sensor to Copyright: © 2022 by the authors. estimate the depth of the images; the second part used inertial sensors (gyroscopes and Licensee MDPI, Basel, Switzerland. accelerometers); and the third part reﬁned the position using simultaneous localization and This article is an open access article mapping (SLAM) technology [1,4,5]. Despite the great potential of the Tango technology, distributed under the terms and Google stopped supporting the project in March 2018. The reasons were probably the conditions of the Creative Commons redundancy of the sensors for the common user and the excessive battery consumption [1]. Attribution (CC BY) license (https:// In 2018, Google replaced Project Tango with ARCore, which detects the depth of environ- creativecommons.org/licenses/by/ ments without active sensors; this is the current technology dedicated to augmented reality 4.0/). Appl. Syst. Innov. 2022, 5, 63. https://doi.org/10.3390/asi5040063 https://www.mdpi.com/journal/asi Appl. Syst. Innov. 2022, 5, 63 2 of 22 on most Android devices. Concerning the iOS market, in 2020, Apple released the iPad Pro 2020 and the iPhone 12 Pro. These devices were the ﬁrst to be equipped with LiDAR scanners and produced interesting research published in the following years. On the basis of these technologies, several experiments were carried out in scanning and modelling indoor and outdoor environments summarised in the following literature review section. 1.1. Literature Review In this section, improvements in smartphone scanning technology are presented in chronological order through the literature review. Diakité & Zlatanova [6] investigated the possibilities of the Google Tango Tablet (a device for developers) to scan and model building interiors to support indoor navigation. The 3D models produced did not have enough detail to support advanced indoor naviga- tion, but simple data processing could integrate basic semantic and topological information into the models. Tomaštík et al. [7] applied Google Tango technology to forest inventory. The authors used the Lenovo Phab 2 Pro to scan three circular tree test areas (radius equal to 12.62 m) differing in age and tree species composition. Point clouds of tree stems were generated from the scans, and tests showed an RMSE of the diameter at breast height (DBH) lower than 0.02 m. Also for forest inventory purposes, Hyyppä et al. [8] used the Lenovo Phab 2 Pro to measure the diameter of individual tree stems. The authors measured 121 tree stem diam- eters using traditional methods and compared them with point clouds generated by the Lenovo Phab 2 Pro. The Lenovo device measurements matched traditional measurements with an RMSE of 0.0073 m and a bias mean of 0.003 m. Mikita et al. [1] applied ARCore technology and a Xiaomi Mi 8 smartphone to survey and model two boulders that were part of rock outcrops in the Trebícsko Nature Park. Al- most all the generated models differed from the reference models (generated by Terrestrial LiDAR Scanning-TLS) by values smaller than 6 cu m for volume and 2 sqm for area. Tsoukalos et al. [9] tested the possibilities of detecting a 12 sqm room with ARCore and EasyAR (a commercial application for augmented reality). At the end of the experiments, the authors were not satisﬁed with the produced models and proposed modiﬁcations and new technologies to improve the 3D models, such as the use of a depth recognition API (recently implemented on a small part of Android devices as DepthAPI) and the use of LiDAR sensors. Vogt et al. [10] investigated the capabilities of Apple devices to scan small objects; the authors used the LiDAR scanner and TrueDepth technology of the iPad Pro to scan LEGO bricks of different shapes. Comparing the results with an industrial 3D scanner (Artec Space Spider), Vogt et al. showed that in all cases, the industrial scanner provides better results, but the accuracy of the smartphone may be sufﬁcient depending on the applications. Spreaﬁco et al. [11] presented research concerning the large-scale 3D mapping capabil- ities of the iPad Pro LiDAR sensor. Focusing on architectural survey applications, Spreaﬁco et al. scanned a scene consisting of an outdoor emergency staircase connected to a historic building. The point cloud captured with the iPad Pro showed a precision of 0.02 m and an accuracy of 0.04 m, which—importantly—is suitable for architectural mapping at a scale of 1:200. Riquelme et al. [12] used an iPhone 12 Pro to scan a 26 m mechanically excavated cretaceous marlstone and limestone rock face and extracted rock discontinuities from the point cloud. Riquelme et al. identiﬁed in their research that the optimal distance for scanning rocks with the iPhone Pro 12 is less than 3 m and, based on their results, highlighted the device’s great potential for detecting rocky slopes. Appl. Syst. Innov. 2022, 5, 63 3 of 22 Luetzenburg et al. [2] investigated the accuracy of the iPad Pro 2020 and iPhone Pro 12 LiDAR scanner in surveying large natural elements for possible applications in the ﬁeld of geosciences. Luetzenburg et al. surveyed and reconstructed in 3D the Roneklint cliff in Denmark. The cliff was 130 m long and had a mean height of 10 m. The 3D model reconstructed by scanning with iPad Pro 2020 and iPhone Pro 12 LiDAR presented an accuracy of 0.1 m. Gollob et al. [3] used the iPad Pro LiDAR scanner for forest inventory purposes. The data acquisition time with the iPad Pro was approximately 7.51 min per sample plot (radius equal to 7 m), trees were mapped with a 97.3% detection rate for trees with a DBH less than 10 cm, and the RMSE of the best DBH measurement was 0.0313 m. Tavani et al. [13] analysed the performance of the iPhone 12 Pro in geoscience-related applications to replace conventional geological instruments. The authors tested the device’s GNSS, IMU, magnetometer, cameras and LiDAR sensor. Regarding the LiDAR sensor, the authors concluded that it was mostly useful for “soft” applications, such as geoheritage documentation and the production of educational materials. 1.2. Aim and Organization of the Paper A review of the literature reveals that research in the ﬁeld of smartphone depth sensors has focused on studying one type of device at a time. There is a clear division between research dedicated to Android and Apple sensors and a general lack of comparative research between these two environments. Furthermore, it can be seen that in recent years, research has neglected the study of ToF cameras mounted on Android devices, even though new applications using ToF have been developed. Based on these considerations, in this manuscript, a suitable method able to investigate the performance offered by depth sensors mounted on Android and Apple devices in various case studies related to urban environments is described. The paper is composed of ﬁve sections. The section “Materials and Methods” contains the smartphones tested (Section 2.1), the research method pipeline (Section 2.2), the case studies discussed (Section 2.2.1) and the analysis method applied to the acquired point clouds (Section 2.2.2). The third section is devoted to the presentation of the results. Section 3.1 shows the results of the tests conducted in the laboratory, while Section 3.2 shows the results of the tests conducted in the ﬁeld. Discussions and conclusions are discussed at the end of the paper. 2. Materials and Methods 2.1. Mobile Devices and Scanning Apps In the experimentation, three devices were used: Huawei P30 Pro (Huawei Technolo- gies Co., Ltd., Shenzhen, China), iPhone 12 Pro and iPAD 2021 Pro (Apple Inc., Cupertino, CA, USA). The devices were selected to represent the (best) LiDAR scanning solutions for Android and iOS operating systems, respectively. Table 1 shows the technical characteristics of the 3 devices taken into consideration in this paper. In smartphone depth sensor scanning solutions, a laser beam with a wavelength in near infrared (NIR) of ~8XX-9XX nm is emitted in a 2D array (e.g., 8 8 points in iOS) by a vertical-cavity surface-emitting-laser (VCSEL). The pulse time of ﬂight (dToF) is measured by a Single Photon Avalanche Photodiode (SPAD). The combination of VCEL and SPAD has made the implementation of ﬂash-LiDAR solutions in smartphones possible [2]. 3D Live Scanner Pro app (Lubos Vonasek Programmierung) was used to perform the scans with the Huawei P30 Pro; this app allows indoor and outdoor scanning available on all Android device equipped with AR (Augmented Reality). Devices equipped with a LiDAR sensor can capture more details during a survey, calculate depth better and generate more accurate 3D scans (e.g., well-deﬁned contours). The maximum resolution of the point cloud generated by 3D Live Scanner Pro is 2 cm. 3D Scanner App™ (Laan Labs) was used in order to perform the scans with the iPhone 12 Pro and iPad 2021 Pro; with this Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 4 of 22 Appl. Appl. Syst Syst . Innov. . Innov. 2022 2022 , 5, x FO , 5, x FO R PR P EER R EER R EVIE EVIE W W 4 of4 of 22 22 Appl. Syst. Innov. 2022, 5, 63 4 of 22 point cloud g point cloud g point cloud g enerated b enerated b enerated b y 3D Live y 3D Live y 3D Live Scann Scann Sc er Pr ann er Pr o e is r Pr o 2 is cm o 2 is cm . 2 3D cm . 3D Sc . 3D anner Scanner Scanner ApAp p™ Ap p (L ™ p aan (L ™aan (L Lab aan Lab s) w Lab s) w as s) w as as used used in order in order to perform the scan to perform the scan s with s with the iP the iP hone 12 hone 12 Pro Pro and iPad 2 and iPad 2 021021 Pro; Pro; with thi with thi s s used in order to perform the scans with the iPhone 12 Pro and iPad 2021 Pro; with this application, 3D models can be generated using LiDAR data and photos: LiDAR scans can applic applic ation ation , 3D , 3 model D model s ca s c n be gene an be gene ratrat ed us ed us ing ing LiD LiD AR dat AR dat a and pho a and pho tos: LiD tos: LiD AR sc AR sc ans c ans c an an application, 3D models can be generated using LiDAR data and photos: LiDAR scans can be made in low resolution (~1.5 cm) and high resolution (1 cm). be made be made in in low reso low reso lutlut ioni on (~1. (~ 5 cm 1.5 cm ) and ) and high high reso reso lutlut ion ( ion ( 1 cm) 1 cm) . . be made in low resolution (~1.5 cm) and high resolution (1 cm). Table 1. Main technical features of the 3 mobile devices used in the experimentation. Table 1. Table 1. Main Main technical technical featu featu res of res of the 3 the 3 mobile mobile devi devi cesces use u ds in ed in the experi the experi mentation. mentation. Table 1. Main technical features of the 3 mobile devices used in the experimentation. Device Huawei P30 Pro iPhone 12 Pro iPad 2021 Pro Device Device Huawei P30 Huawei P30 Pro Pro iPhone 12 iPhone 12 Pro Pro iPad 2021 iPad 2021 Pro Pro Device Huawei P30 Pro iPhone 12 Pro iPad 2021 Pro Imag Im e ag Image e Image Chipset Huawei HiSilicon Kirin 980 Apple A14 Bionic Apple M1 Chipset Chipset Chipset Huawei HiS Huawei HiS Huawei HiS ilico iln Kirin ico iln Kirin icon Kirin 980980 980 Apple A14 Apple A14 Apple A14 Bionic Bionic Bionic Apple M1 Apple M1 Apple M1 RAM 8 GB 6 GB 8 GB RAM RAM 8 GB 8 GB 6 GB 6 GB 8 GB 8 GB RAM 8 GB 6 GB 8 GB Original operative system Android 9 EMUI 9.1 Pie iOS 14 iOS 14 Original operative system Android 9 EMUI 9.1 Pie iOS 14 iOS 14 Original operat Original operat ive ive system system Android 9 EM Android 9 EM UI 9 UI 9 .1 Pie .1 Pie iOS 14 iOS 14 iOS 14 iOS 14 Digital Camera 40 Mp + 20 Mp + 8 Mp 12 Mp + 12 Mp + 12 Mp 12MP + 10MP Digital Camera Digital Camera 40 Mp + 20 40 Mp + 20 Mp Mp + 8 + 8 Mp Mp 12 Mp + 12 12 Mp + 12 Mp Mp + 12 + 12 Mp Mp 12MP + 12MP + 10MP 10MP Digital Camera Aperture Size 40 Mp + 20 F 1.6 Mp + + 8 F 2.2 Mp + F 3.4 12 Mp + 12 F 1.6 Mp + F + 12 2.4 + Mp F 2 12MP + F 1.8 + 10MP F 2.4 Depth sensor Sony IMX316 (ToF) Sony IMX590 Sony IMX590 Aperture Size Aperture Size F 1.6 + F 1.6 + F 2.2 F 2.2 + F + F 3.4 3. 4 F 1.6 + F 1.6 + F 2.4 F 2.4 + F + F 2 2 F 1.8 + F 1.8 + F 2.4 F 2.4 Aperture Size F 1.6 + F 2.2 + F 3.4 F 1.6 + F 2.4 + F 2 F 1.8 + F 2.4 GNSS GPS, GLONASS, BeiDou, GPS, GLONASS, BeiDou, GPS, GLONASS, BeiDou, Depth sensor Sony IMX316 (ToF) Sony IMX590 Sony IMX590 Depth sensor Depth sensor Sony IMX316 ( Sony IMX316 ( ToF) ToF) Sony IMX590 Sony IMX590 Sony IMX590 Sony IMX590 Constellation Galileo, QZSS Galileo, QZSS Galileo, QZSS GNSS GNSS GPGP S, GL S, GL ON ON ASS, A S B S, e iB Deou, iDou, GPGP S, GL S, GL ON ON ASS, A S B S, e iB Deou, iDou, GPGP S, GL S, GL ON ON ASS, A S B S, e iB Deou, iDou, GNSS GPS, GLONASS, BeiDou, GPS, GLONASS, BeiDou, GPS, GLONASS, BeiDou, Frequency L1/L5 L1/L5 L1/L5 Constellation Constellation Galileo, Galileo, QZSS QZSS Galileo, Galileo, QZSS QZSS Galileo, Galileo, QZSS QZSS Constellation Galileo, QZSS Galileo, QZSS Galileo, QZSS Inertial Accelerometer, gyroscope, Accelerometer, gyroscope, Accelerometer, gyroscope, Frequency L1/L5 L1/L5 L1/L5 Frequency L1/L5 Frequency L1/L5 sensors magnetometer magnetometerL1/L5L1/L5 magnetometerL1/L5L1/L5 Weight 191 g 189 g 466 g Inertial Inertial Acce Acce lerom lerom eter, eter, gy g roscope, yroscope, Acce Acce lerom lerom eter, eter, gy g roscope, yroscope, Acce Acce lerom lerom eter, eter, gy g roscope, yroscope, Inertial Accelerometer, gyroscope, Accelerometer, gyroscope, Accelerometer, gyroscope, Dimensions 158 73 8 mm 146.7 71.5 7.4 mm 247.6 178.5 5.9 mm sensors sensors magnetometer magnetometer magnetometer magnetometer magnetometer magnetometer sensors magnetometer magnetometer magnetometer Weight Weight Weight 191 g 191 g 191 g 189 g 189 g 189 g 466 g 466 g 466 g 2.2. Research Methodology Dimensions Dimensions 158 × 73 158 × 73 × 8 × 8 mm mm 146.7 × 146.7 × 71.5 71.5 × 7 × 7 .4 mm .4 mm 247.6 × 247.6 × 178.5 178.5 × 5.9 mm × 5.9 mm Dimensions 158 × 73 × 8 mm 146.7 × 71.5 × 7.4 mm 247.6 × 178.5 × 5.9 mm The research method used in this work consists of two main phases: (i) 3D survey by smartphone depth sensors and (ii) analysis of the acquired point clouds. 2.2. Res 2.2. Res earch earch Metho Metho dology dology 2.2. Research Methodology In phase 1, it is necessary to perform the 3D LiDAR survey to obtain the 3D point The rese The rese The rese arch arch met arch met hod used met hod used hod used in tin t his wor in t his wor his wor k ck o c nsist k o c nsist o snsist of s t o w s f t o ow main ph f t ow main ph o main ph asease s: (i ase s) 3D : (i s) 3D : (i su ) 3D su rvey su rvey by rvey by by cloud of the objects taken into consideration. In order to analyse the quality of the 3D smartphone smartphone depth sensors and (ii) depth sensors and (ii) ana ana lyslis y of t sis of t he ac he ac quire qud ire point d point clouds. clouds. smartphone depth sensors and (ii) analysis of the acquired point clouds. point cloud as the type of object investigated varied, four case studies were analysed. The In p In p hashe as 1, e it 1, i it s nec is nec esse ar ssy arty o p toe p rform erform th t e h 3D L e 3D L iDA iDA R survey R survey to t ob o ob tain ta t in h t e h 3D e 3D po p int oint In phase 1, it is necessary to perform the 3D LiDAR survey to obtain the 3D point scans were performed with the three mobile devices and the apps previously described cloud of the objects ta cloud of the objects ta ken into consi ken into consi derati derati on. In on. In order order to a to a naln yse the qual alyse the qual ity of the 3 ity of the 3 D poi D poi nt nt cloud of the objects taken into consideration. In order to analyse the quality of the 3D point in Section 2.1: 3D Live Scanner Pro for Android and 3D Scanner App™ for iOS. With the cloud as the type of object cloud as the type of object Huawei P30 Pro, the scans investigated v wer investigated v e performed aried, with aried, fo a ur c r fo esolution ur c ase st ase st udie of 2 udie cm, s w s w while ere eanalys re with analys the ed.ed. iPhone The sc The sc ans ans cloud as the type of object investigated varied, four case studies were analysed. The scans 12 Pro and iPad 2021 Pro, the scans were performed with resolutions of 1.5 cm and 1 cm. were per were per form form ed with ed with the t the t hree mobile hree mobile devices and devices and the apps previo the apps previo usly d usly d escr escr ibeid in bed in Se Se c- c- were performed with the three mobile devices and the apps previously described in Sec- In phase 2, the point clouds were segmented in order to isolate the scanned objects; tion tion 2.1: 2. 3D 1: 3D Live Sc Live Sc anner anner Pro for Pro for Androi Androi d an d an d 3D d 3D Scan Scner Ap anner Ap p™ p for ™ for iO iO S. W S. W ithi t th he t H heu H awei uawei tion 2.1: 3D Live Scanner Pro for Android and 3D Scanner App™ for iOS. With the Huawei subsequently, the point clouds were analysed by mathematical descriptors able to identify P30 P3 Pro, 0 Pro, the s thc e s ans cans were were pe p rfe orm rform ed w ed w ithi a re th a re soluti soluti on of on of 2 cm, whil 2 cm, whil e wi e th the iPhone 12 Pr with the iPhone 12 Pr o o P30 Pro, the scans were performed with a resolution of 2 cm, while with the iPhone 12 Pro the level of quality of the 3D model. and iPad 2021 Pro, the scans were performed with resolutions of 1.5 cm and 1 cm. and an iP d aid P a 20 d 21 20 Pro, the 21 Pro, the scans were perf scans were perf ormed wi ormed wi th resol th resol utiu ons of tions of 1.5 1.5 cm cm an a d 1 cm. nd 1 cm. Finally, a visual analysis was used to identify anomalies in point cloud. The software In pha In pha In pha se 2 se , the s 2e , the 2 poi , the poi nt cl poi nt cl ou nt cl ds ou we ds ou we ds re we s re eg s rm e e s g ent m egent ed in ment ed in order ed in order order to isolate the to isolate the to isolate the scanned obje scanned obje scanned obje cts; cts; cts; used for data analysis was CloudCompare (DF R&D/TELECOM Pari-sTech ENST-TSI, subse subse subse quently, the point clo quently, the point clo quently, the point clo uds we uds we uds we re analysed by re analysed by re analysed by mat mat h mat emat hemat h ic emat al d ical d e ic sc al d e ript sce ript sc ors ript ors able t ors able t o able t ident o ident o ident ify ify ify Paris, France) [14]. the level of quality o the level of quality o f the f the 3D model. 3D model. the level of quality of the 3D model. An overview of the pipeline of the research methodology is shown in Figure 1; in the following Fina Fina lly, lla vis y, sections, a vis ualu an aeach l an alys al step is yswas is will was used t beused t described o ident o ident in ify detail. ify anom anom alies alies in point in point cloud cloud . The so . The so ftware ftware Finally, a visual analysis was used to identify anomalies in point cloud. The software used used for for da d ta an ata an alysis w alysis w as CloudCompare as CloudCompare (DF (DF R&D R&D /TELECOM Par /TELECOM Par i-sTech i-sTech ENEST-T NST-T SI, SI, used for data analysis was CloudCompare (DF R&D/TELECOM Pari-sTech ENST-TSI, Par Par is, Fr is, Fr ance ance ) [14]. ) [14]. Paris, France) [14]. An overv An overv iew iew of the of the pipe pipe line of the line of the resear resear ch ch methodolo methodolo gy is gy is shown shown in in Figure Figure 1; in 1; in the th e An overview of the pipeline of the research methodology is shown in Figure 1; in the foll fol owing lowing sec sec tions, e tions, e ach ach step wil step wil l be l be des des cribed cribed in det in det ail.a il. following sections, each step will be described in detail. Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 5 of 22 Appl. Syst. Innov. 2022, 5, 63 5 of 22 Figure 1. Figure 1.Pipe Pipeline line of the re of the resear sear ch ch methodolog methodology. y. 2.2.1. Phase 1: 3D Survey by Smartphone Depth Sensors 2.2.1. Phase 1: 3D Survey by Smartphone Depth Sensors In phase 1, 3D surveys of several objects in both indoor and outdoor environments In phase 1, 3D surveys of several objects in both indoor and outdoor environments were carried out. In particular, four case studies were taken into consideration. were carried out. In particular, four case studies were taken into consideration. The ﬁrst case study focused on the scanning of some building material samples. The The fir tests wer st case e performed study focu to evaluate sed on the sc the scanning anningperformance of some building ma of depth sensors terial sa under mples. The controlled laboratory conditions and reduced sensor movement to a minimum. This test tests were performed to evaluate the scanning performance of depth sensors under con- was carried out in the geomatics laboratory of the Polytechnic University of Bari, Taranto, trolled laboratory conditions and reduced sensor movement to a minimum. This test was 0 00 0 00 Italy (': 40 31 36 N; : 17 16 60 E). We chose to scan several samples of building carried out in the geomatics laboratory of the Polytechnic University of Bari, Taranto, Italy materials in an urban environment. In this test, we considered albedo as a parameter (φ: 40°31′36″ N; λ: 17°16′60″ E). We chose to scan several samples of building materials in characterising the material. The measurement of albedo was documented in [15]. We an urban environment. In this test, we considered albedo as a parameter characterising performed the scans by keeping the mobile devices in the nadir direction to the samples the material. The measurement of albedo was documented in [15]. We performed the and moving them as little as possible (near-static conditions). We scanned the samples scans by keeping the mobile devices in the nadir direction to the samples and moving from distances of 0.5 m and 1.5 m and with resolutions of 2 cm (Android), 1.5 cm (iOS) them and 1 as little cm (iOS). as po Byssib combining le (nearscan -static distances conditions (2) and ). We s scan canned resolutions the sa (3), mples from we obtained distances 6 different point clouds for each sample. With 10 samples available, a data set of 60 point of 0.5 m and 1.5 m and with resolutions of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). clouds of samples differing in acquisition distance, resolution and material type (albedo) By combining scan distances (2) and scan resolutions (3), we obtained 6 different point was obtained. In Table 2, we report the features of each sample—dimensions, area, material clouds for each sample. With 10 samples available, a data set of 60 point clouds of samples and albedo—while in Figure 2, an overview of the scanned samples is reported. differing in acquisition distance, resolution and material type (albedo) was obtained. In Table 2, we report the features of each sample—dimensions, area, material and albedo— Table 2. Features of the material samples studied. while in Figure 2, an overview of the scanned samples is reported. Sample Material Dimensions [m] Area [sqm] Albedo Table 2. Features of the material samples studied. Smooth cement plaster 0.4 0.45 0.05 0.180 0.514 Raw cement plaster 0.4 0.45 0.05 0.180 0.524 Sample Material Dimensions [m] Area [sqm] Albedo White lime plaster 0.505 0.365 0.01 0.184 0.518 Coloured lime plaster 0.525 0.47 0.01 0.247 0.510 Smooth cement plaster 0.4 × 0.45 × 0.05 0.180 0.514 Tetraﬂuoroethylene (TFE) 0.245 0.19 0.001 0.046 0.455 Raw cement plaster 0.4 × 0.45 × 0.05 0.180 0.524 Methacrylate (PMMA) 0.305 0.12 0.005 0.037 0.433 High-density White polyethylene lime plast(HDPE) er 0.23 0. 50 0.22 5 × 0. 0.002 365 × 0.01 0.051 0.184 0.623 0.518 Frosted glass 0.3 0.3 0.005 0.090 0.517 Coloured lime plaster 0.525 × 0.47 × 0.01 0.247 0.510 Steel 0.205 0.3 0.008 0.061 0.606 Tetrafluoroethylene (TFE) 0.245 × 0.19 × 0.001 0.046 0.455 Brass 0.105 0.303 0.009 0.032 0.661 Methacrylate (PMMA) 0.305 × 0.12 × 0.005 0.037 0.433 High-density polyethylene (HDPE) 0.23 × 0.22 × 0.002 0.051 0.623 Frosted glass 0.3 × 0.3 × 0.005 0.090 0.517 Steel 0.205 × 0.3 × 0.008 0.061 0.606 Brass 0.105 × 0.303 × 0.009 0.032 0.661 Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 6 of 22 Appl. Syst. Innov. 2022, 5, 63 6 of 22 Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 6 of 22 (a) (b) (c) (d) (e) (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (f) (g) (h) (i) (j) Figure 2. Set of scanned samples in the first test: smooth cement plaster (a), raw cement plaster (b), Figure 2. Set of scanned samples in the first test: smooth cement plaster (a), raw cement plaster (b), white lime plaster (c), coloured lime plaster (d), tetrafluoroethylene (TFE) (e), methacrylate (PMMA) Figure 2. Set of scanned samples in the ﬁrst test: smooth cement plaster (a), raw cement plas- white lime plaster (c), coloured lime plaster (d), tetrafluoroethylene (TFE) (e), methacrylate (PMMA) (f), high-density polyethylene (HDPE) (g), frosted glass (h), steel (i), brass (j). ter (b), white lime plaster (c), coloured lime plaster (d), tetraﬂuoroethylene (TFE) (e), methacrylate (f), high-density polyethylene (HDPE) (g), frosted glass (h), steel (i), brass (j). (PMMA) (f), high-density polyethylene (HDPE) (g), frosted glass (h), steel (i), brass (j). In the second case study, a statue in public gardens was scanned. The test was per- In the second case study, a statue in public gardens was scanned. The test was per- In the second case study, a statue in public gardens was scanned. The test was formed to investigate how sensor movement could generate aberrations in the scans. The formed to investigate how sensor movement could generate aberrations in the scans. The performed to investigate how sensor movement could generate aberrations in the scans. second test was performed in the public gardens of “Villa Peripato” in the city of Taranto, second test was performed in the public gardens of “Villa Peripato” in the city of Taranto, The second test was performed in the public gardens of “Villa Peripato” in the city of Italy (φ: 40°28′20″ N; λ: 17°14′80″ E). In the Villa Peripato, we scanned a statue represent- Italy (φ: 40°28′20″ N; λ: 17°14′80″ E). In the Villa Peripato, we scanned a statue represent- 0 00 0 00 Taranto, Italy (': 40 28 20 N; : 17 14 80 E). In the Villa Peripato, we scanned a statue ing an animal (Figure 3). The scans were realised by performing concentric circles to the ing an animal (Figure 3). The scans were realised by performing concentric circles to the representing an animal (Figure 3). The scans were realised by performing concentric circles statue at fixed distances of 2 m and 3 m. During the scans, the smartphones were main- statue at fixed distances of 2 m and 3 m. During the scans, the smartphones were main- to the statue at ﬁxed distances of 2 m and 3 m. During the scans, the smartphones were tained parallel to the statue and worked at resolutions of 2 cm (Android) and 1.5 cm (iOS). tained parallel to the statue and worked at resolutions of 2 cm (Android) and 1.5 cm (iOS). maintained parallel to the statue and worked at resolutions of 2 cm (Android) and 1.5 cm From this test, we obtained a dataset consisting of four point clouds that differed in ac- From this test, we obtained a dataset consisting of four point clouds that differed in ac- (iOS). From this test, we obtained a dataset consisting of four point clouds that differed in quisition distance and resolution. acquisition quisition d distance istance and reso and resolution. lution. Figure 3. Survey of the statue (second test). In the third test, we scanned a room inside the geomatics laboratory ([16]—Figure 4) Figure 3. Survey of the statue (second test). Figure 3. Survey of the statue (second test). to verify the performance of depth sensors in scanning an object from the inside. The room has a simple parallelepiped shape with dimensions of 4.85 × 4.15 × 2.95 m and contains In the third test, we scanned a room inside the geomatics laboratory ([16]—Figure 4) three cabinets and an air conditioner. To perform the test, the windows were screened to verify the performance of depth sensors in scanning an object from the inside. The room has a simple parallelepiped shape with dimensions of 4.85 × 4.15 × 2.95 m and contains three cabinets and an air conditioner. To perform the test, the windows were screened Appl. Syst. Innov. 2022, 5, 63 7 of 22 Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 7 of 22 In the third test, we scanned a room inside the geomatics laboratory ([16]—Figure 4) to verify the performance of depth sensors in scanning an object from the inside. The room has a simple parallelepiped shape with dimensions of 4.85 4.15 2.95 m and contains three cabinets and an air conditioner. To perform the test, the windows were screened with with sheets of paper. To perform the scans, we walked around the room, acquiring in sheets of paper. To perform the scans, we walked around the room, acquiring in order: order: the walls, the floor and the ceiling. In this test, we carried out three scans with res- the walls, the ﬂoor and the ceiling. In this test, we carried out three scans with resolutions olutions of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). We created a dataset consisting of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). We created a dataset consisting of three point of three po clouds.int clouds. Figure 4. Detail of the room scanned in the third test. Figure 4. Detail of the room scanned in the third test. In the fourth case study, the bases of a Doric column were scanned in an archaeological In the fourth case study, the bases of a Doric column were scanned in an archaeolog- site in order to investigate the performance of the sensors in scanning a complex free- ical site in order to investigate the performance of the sensors in scanning a complex free- form object. In particular, the objects taken into consideration concern an archaeological form object. In particular, the objects taken into consideration concern an archaeological 0 00 0 00 site located in the historic centre of Taranto, Italy (': 40 28 26 N; : 17 13 59 E). This site located in the historic centre of Taranto, Italy (φ: 40°28′26″ N; λ: 17°13′59″ E). This site site contains two Doric columns and the remains of a third column consisting of a base contains two Doric columns and the remains of a third column consisting of a base and 3 and 3 column drums (Figure 5a); the columns are dated to the 6th century BC. In the column drums (Figure 5a); the columns are dated to the 6th century BC. In the test, we test, we scanned the remains of the third column (Figure 5b). To perform the scans, we made concentric circles around the remains of the column with a variable distance and scanned the remains of the third column (Figure 5b). To perform the scans, we made con- tried to capture every detail of the object with smartphones. Three scans were taken with centric circles around the remains of the column with a variable distance and tried to cap- resolutions of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). In this way, it was possible to ture every detail of the object with smartphones. Three scans were taken with resolutions build a dataset consisting of three point clouds. of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). In this way, it was possible to build a dataset consisting of three point clouds. 2.2.2. Phase 2: Point Clouds Analysis At this phase, the point clouds were segmented in order to isolate the object of experimentation. If segmentation was not carried out, the values of the mathematical descriptors could be altered in the subsequent processing phase. After the segmentation step, the point clouds were analysed by mathematical descriptors. Indeed, the geometric features of a point cloud can be described by applying a principal component analysis (PCA) to the covariance matrix C of the neighbourhood p of a point p of a set of points N . The covariance matrix C (Equation (1)) can be deﬁned as a three-dimensional tensor containing the geometric information of a set of points N in the neighbourhood p [17,18]. 2 3 2 3 p p p p i i 1 1 4 5 4 5 C = . . . . . . . . . . . . , i 2 N (1) j p p p p p i i k k (a) (b) Figure 5. Overview of the Doric columns of Taranto (a); remains of the third column used for the fourth test (b). Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 7 of 22 with sheets of paper. To perform the scans, we walked around the room, acquiring in order: the walls, the floor and the ceiling. In this test, we carried out three scans with res- olutions of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). We created a dataset consisting of three point clouds. Figure 4. Detail of the room scanned in the third test. In the fourth case study, the bases of a Doric column were scanned in an archaeolog- ical site in order to investigate the performance of the sensors in scanning a complex free- Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 8 of 22 form object. In particular, the objects taken into consideration concern an archaeological site located in the historic centre of Taranto, Italy (φ: 40°28′26″ N; λ: 17°13′59″ E). This site Appl. Syst. Innov. 2022, 5, 63 8 of 22 contains two Doric columns and the remains of a third column consisting of a base and 3 column drums (Figure 5a); the columns are dated to the 6th century BC. In the test, we 2.2.2. Phase 2: Point Clouds Analysis scanned the remains of the third column (Figure 5b). To perform the scans, we made con- At this phase, the point clouds were segmented in order to isolate the object of exper- where p is the centroid of a neighbourhood, p , of a set of points N (Figure 6). Considering centric circles around the remains of the column with a variable distance and tried to cap- i p imentation. If segmentation was not carried out, the values of the mathematical de- an eigenvector problem, it can be written: ture every detail of the object with smartphones. Three scans were taken with resolutions scriptors could be altered in the subsequent processing phase. After the segmentation of 2 cm (Android), 1.5 cm (iOS) and 1 cm (iOS). In this way, it was possible to build a step, the point clouds were anaC lysed by v = l v ma , lth2ematic f1, 2, al desc 3g riptors. Indeed, the ge(2) ometric l l l dataset consisting of three point clouds. features of a point cloud can be described by applying a principal component analysis (PCA) to the covariance matrix C of the neighbourhood 𝑝 of a point 𝑝 ̅ of a set of points Np. The covariance matrix C (Equation (1)) can be defined as a three-dimensional tensor containing the geometric information of a set of points Np in the neighbourhood 𝑝 [17,18]. 𝑝 −𝑝 ̅ 𝑝 −𝑝 ̅ ... ... ... ... (1) 𝐶= ∙ ,𝑖 ∈𝑁 𝑝 −𝑝 ̅ 𝑝 −𝑝 ̅ where 𝑝 ̅ is the centroid of a neighbourhood, 𝑝 , of a set of points Np (Figure 6). Consid- ering an eigenvector problem, it can be written: 𝐶∙𝑣 =𝜆 ∙𝑣 ,𝑙 ∈ 1,2,3 (2) The covariance matrix C is symmetrical and positive semidefinite, and using a PCA, it is possible to extract the three eigenvalues 𝜆 ,𝜆 ,𝜆 [19] from the matrix. The eigenval- (a) (b) ues locally describe the 3D structure of the point set Np and quantify its variation along Figure 5. Overview of the Doric columns of Taranto (a); remains of the third column used for the Figure 5. Overview of the Doric columns of Taranto (a); remains of the third column used for the the direction of the corresponding eigenvector 𝑣 ,𝑣 ,𝑣 (Figure 6). fourth test (b). fourth test (b). Figure 6. Neighbourhood and covariance analysis, images adapted from [18]. Figure 6. Neighbourhood and covariance analysis, images adapted from [18]. The covariance matrix C is symmetrical and positive semideﬁnite, and using a PCA, it Through mathematical operations between the three eigenvalues, it is possible to cal- is possible to extract the three eigenvalues l , l , l [19] from the matrix. The eigenvalues 1 2 3 locally describe the 3D structure of the point set N and quantify its variation along the culate certain form-specific mathematical descriptors called covariance-features or eigen- direction of the corresponding eigenvector v , v , v (Figure 6). 2 3 features that are capable of describing certain g 1 eometric characteristics of the point cloud. Through mathematical operations between the three eigenvalues, it is possible to For example, some descriptors found in the literature are linearity, planarity, anisotropy, calculate certain form-speciﬁc mathematical descriptors called covariance-features or eigen- omnivariance, eigentropy and surface variation and sphericity [17,18,20]. In this research, features that are capable of describing certain geometric characteristics of the point cloud. we used the eigenfeatures of planarity, pmnivariance and surface variation to analyse For example, some descriptors found in the literature are linearity, planarity, anisotropy, point clouds based on their shape (eigenfeature analysis). The equations of the three latter omnivariance, eigentropy and surface variation and sphericity [17,18,20]. In this research, descriptors are: we used the eigenfeatures of planarity, pmnivariance and surface variation to analyse point clouds based on their shape (eigenfeature analysis). The equations of the three latter Planarity 𝑃 = (3) descriptors are: l l 2 3 Planarity P = (3) (4) Omnivariance 𝑂 =(𝜆 ∙𝜆 ∙𝜆 ) Omnivariance O = (l l l ) (4) 2 3 l 1 Surface Variation = l (5) Surface Variation SV = (5) l + l + l 1 2 3 Planarity quantitatively describes the tendency of the point cloud to arrange itself along plane surfaces. This eigenfeature can be used to describe point clouds of planar ob- jects or those formed by a combination of planes. For this reason, during data analysis, we 𝑆𝑉 Appl. Syst. Innov. 2022, 5, 63 9 of 22 Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 9 of 22 Planarity quantitatively describes the tendency of the point cloud to arrange itself along plane surfaces. This eigenfeature can be used to describe point clouds of planar objects used Pla orn those arity a formed s a descri byp ator f combination or the 10 sa ofmpl planes. es in the f For this irst test a reason, nd the room i during datananalysis, the third we test. used Planarity as a descriptor for the 10 samples in the ﬁrst test and the room in the third test. Omnivariance quantifies the degree of inhomogeneity of the point cloud in 3 dimen- Omnivariance quantiﬁes the degree of inhomogeneity of the point cloud in 3 dimen- sions. This descriptor can be used to describe the point clouds of freeform objects, and in sions. This descriptor can be used to describe the point clouds of freeform objects, and in this work, we applied omnivariance to the statue in the second test and to the remains of this work, we applied omnivariance to the statue in the second test and to the remains of the column in the fourth test. the column in the fourth test. Surface variation quantitatively describes the variations along the normal to the sur- Surface variation quantitatively describes the variations along the normal to the face of a point cloud and, as demonstrated in Pauly et al. [18], can be used to identify two surface of a point cloud and, as demonstrated in Pauly et al. [18], can be used to identify point clouds side-by-side and overlapping at a certain distance (Figure 7a). Figure 7b two point clouds side-by-side and overlapping at a certain distance (Figure 7a). Figure 7b shows the descriptor applied to two flat point clouds that are side-by-side with their cor- shows the descriptor applied to two ﬂat point clouds that are side-by-side with their corners ners overlapping (the orange ellipse indicates the green overlapping parts identified with overlapping (the orange ellipse indicates the green overlapping parts identiﬁed with surface surface variation). For these reasons, we used surface variation in all four case studies to variation). For these reasons, we used surface variation in all four case studies to identify identify and try to quantify the phenomenon of smartphone point clouds surface splitting. and try to quantify the phenomenon of smartphone point clouds surface splitting. (a) (b) Figure 7. Functioning of the surface variation descriptor (image adapted from Pauly et al. [18]) (a); Figure 7. Functioning of the surface variation descriptor (image adapted from Pauly et al. [18]) (a); example of surface variation application in a generic case (b). example of surface variation application in a generic case (b). Finally, a visual analysis of the point clouds was performed to confirm and visually Finally, a visual analysis of the point clouds was performed to conﬁrm and visually highlight anomalies and problems in the scans. The analysis was performed by slicing the highlight anomalies and problems in the scans. The analysis was performed by slicing the point clouds in some critical parts indicated by the scalar field generated by the previous point clouds in some critical parts indicated by the scalar ﬁeld generated by the previous eigenfeature analysis. This made the visual analysis easier and more effective. eigenfeature analysis. This made the visual analysis easier and more effective. 3. Results 3. Results In this section, we report the results obtained in the four case studies. The results In this section, we report the results obtained in the four case studies. The results were divided into two sections: the ﬁrst section is dedicated to the tests performed in were divided into two sections: the first section is dedicated to the tests performed in the the laboratory; the second section is dedicated to the ﬁeld tests and in real conditions. laboratory; the second section is dedicated to the field tests and in real conditions. Since Since the LiDAR depth sensor of the iOS devices (iPhone 12 Pro and iPad 2021 Pro) is the the LiDAR depth sensor of the iOS devices (iPhone 12 Pro and iPad 2021 Pro) is the same same (Table 1), in this section, we will only discuss the most signiﬁcant point clouds of the (Table 1), in this section, we will only discuss the most significant point clouds of the two two devices. devices. 3.1. Laboratory Testing under Controlled Conditions 3.1. Laboratory Testing under Controlled Conditions In the ﬁrst case study, 10 samples of building material were scanned. We reported In the first case study, 10 samples of building material were scanned. We reported some examples of the point clouds acquired with 1 cm resolution at a distance of 0.5 m in some examples of the point clouds acquired with 1 cm resolution at a distance of 0.5 m in Appendix A (Figure A1). Appendix A (Figure A1). Operating on planar samples, it was possible to observe the structure of the point Operating on planar samples, it was possible to observe the structure of the point cloud generated by smartphone apps. As can be seen in Figure 8, when iOS scanned cloud generated by smartphone apps. As can be seen in Figure 8, when iOS scanned with with 1 cm resolution (Figure 8a), it generated an ordered point cloud; when working with 1 cm resolution (Figure 8a), it generated an ordered point cloud; when working with ~1.5 ~1.5 cm resolution, the device generated a disordered cloud (Figure 8b). Android’s point cm resolution, the device generated a disordered cloud (Figure 8b). Android’s point cloud, cloud, shown in Figure 8c, was as ordered as the iOS one in Figure 8a, but of course had a shown in Figure 8c, was as ordered as the iOS one in Figure 8a, but of course had a differ- different resolution. ent resolution. Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 10 of 22 Appl. Syst. Innov. 2022, 5, 63 10 of 22 Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 10 of 22 (a) (b) (c) Figure 8. Point cloud structure: iOS resolution 1 cm (a), iOS resolution ~1.5 cm (b), Android resolu- (a) (b) (c) tion 2 cm (c). Figure 8. Point cloud structure: iOS resolution 1 cm (a), iOS resolution ~1.5 cm (b), Android resolu- Figure 8. Point cloud structure: iOS resolution 1 cm (a), iOS resolution ~1.5 cm (b), Android resolution tion 2 cm (c). 2 cm (c). We calculated the mean values of surface variation and planarity for the 60 point clouds obtained from the sample scans. The tables containing the surface variation and We calculated the mean values of surface variation and planarity for the 60 point We calculated the mean values of surface variation and planarity for the 60 point planarity values can be found in Appendix A (Tables A1 and A2), while below, we pro- clouds obtained from the sample scans. The tables containing the surface variation and clouds obtained from the sample scans. The tables containing the surface variation and pose the data in graphic form for easier exploration and evaluation. planarity values can be found in Appendix A (Tables A1 and A2), while below, we pro- planarity values can be found in Appendix A (Tables A1 and A2), while below, we propose pose the data in graphic form for easier exploration and evaluation. In Figure 9, we reported the calculated surface variation values for almost all samples the data in graphic form for easier exploration and evaluation. In Figure 9, we reported the calculated surface variation values for almost all samples scanned In at Figur a dist e 9 anc , we e of reported 0.5 m.the Spcalculated ecial cases surface of stee variation l and bras values s will for be disc almost usse all samples d separately scanned at a distance of 0.5 m. Special cases of steel and brass will be discussed separately scanned at a distance of 0.5 m. Special cases of steel and brass will be discussed separately in Section 4. SVλ values obtained from the materials shown in Figure 9 can be considered in Section 4. SVλ values obtained from the materials shown in Figure 9 can be considered in Section 4. SV values obtained from the materials shown in Figure 9 can be considered excellent, as they are inferior to 0.33, the theoretical maximum value of surface variation excellent, as they are inferior to 0.33, the theoretical maximum value of surface variation excellent, as they are inferior to 0.33, the theoretical maximum value of surface variation [18]. [18]. The surface variation values of the samples scanned at 1.5 m were reported in Ap- [18]. The surface variation values of the samples scanned at 1.5 m were reported in Ap- The surface variation values of the samples scanned at 1.5 m were reported in Appendix A pendix A (Figure A2). The plaster, plastic and frosted glass data confirmed the excellent pendix A (Figure A2). The plaster, plastic and frosted glass data confirmed the excellent (Figure A2). The plaster, plastic and frosted glass data conﬁrmed the excellent values values obtained for the 0.5 m scans. obtained values obt for ain the ed for 0.5 m the scans. 0.5 m scans. Figure 9. Comparison of mean surface variation values for samples scanned at a distance of 0.5 m. Figure 9. Comparison of mean surface variation values for samples scanned at a distance of 0.5 m. Figure 9. Comparison of mean surface variation values for samples scanned at a distance of 0.5 m. In Figure 10, we reported the planarity values of the samples scanned at 0.5 m dis- tance, while the values of the samples scanned at 1.5 m distance were reported in appen- In Figure 10, we reported the planarity values of the samples scanned at 0.5 m distance, In Figure 10, we reported the planarity values of the samples scanned at 0.5 m dis- dix A (Figure A3). From the planarity analysis, it can be seen that they take on a value while the values of the samples scanned at 1.5 m distance were reported in Appendix A tance, while the values of the samples scanned at 1.5 m distance were reported in appen- close to unity and thus the tendency of the point cloud to dispose along planar surfaces. (Figure A3). From the planarity analysis, it can be seen that they take on a value close to dix A (Figure A3). From the planarity analysis, it can be seen that they take on a value Finally, we investigated the existence of a possible linear correlation between the albedo unity and thus the tendency of the point cloud to dispose along planar surfaces. Finally, close to unity and thus the tendency of the point cloud to dispose along planar surfaces. of the materials and their surface variation values. we investigated the existence of a possible linear correlation between the albedo of the 2 2 Finally, we investigated the existence of a possible linear correlation between the albedo In Table 3, we reported the R values obtained. In statistics, the value of R (called materials and their surface variation values. coefficient of determination) is a coefficient that indicates how much the variation of a of the materials and their surface variation values. 2 2 dependent variable may depend on the variation of an independent variable in a regres- In Table 3, we reported the R values obtained. In statistics, the value of R (called sion model. R values between 0.7 and 0.5 indicate a moderate relationship [21,22], so we coefficient of determination) is a coefficient that indicates how much the variation of a concluded that there was a moderate-to-weak relationship between albedo and surface dependent variable may depend on the variation of an independent variable in a regres- sion model. R values between 0.7 and 0.5 indicate a moderate relationship [21,22], so we concluded that there was a moderate-to-weak relationship between albedo and surface Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 11 of 22 variation for the LiDAR sensor of the iOS devices. In contrast, the relationship for the Appl. Syst. Innov. 2022, 5, 63 11 of 22 Android device appeared weaker. Figure 10. Figure 10. Comparison of m Comparison of mean ean planarity values for planarity values for samples sample scanned s scanned at at a distance a distance of 0.5 of 0 m. .5 m. 2 2 In Table 3, we reported the R values obtained. In statistics, the value of R (called Table 3. R2 values obtained by linear regression between sample albedo and surface variation val- coefﬁcient of determination) is a coefﬁcient that indicates how much the variation of a ues. dependent variable may depend on the variation of an independent variable in a regression 2 2 model. R values between 0.7 and 0.5 indicate a moderate relationship [21,22], so we Resolution R for Scanning Distance: 0.5 m R for Scanning Distance: 1.5 m concluded that there was a moderate-to-weak relationship between albedo and surface 1 cm (iOS) 0.54 0.68 variation for the LiDAR sensor of the iOS devices. In contrast, the relationship for the 1.5 cm (iOS) 0.53 0.40 Android device appeared weaker. 2 cm (Android) 0.27 0.20 Table 3. R values obtained by linear regression between sample albedo and surface variation values. 3.2. Field Tests and Applications under Real Conditions 2 2 R for Scanning Distance: R for Scanning Distance: Resolution In this section, we reported the data analysis of field tests and performed under real 0.5 m 1.5 m and dynamic conditions (second, third and fourth case study). Examples of the scans of 1 cm (iOS) 0.54 0.68 the statue, the room and the rests of the Doric column are presented in Appendix B (Figure 1.5 cm (iOS) 0.53 0.40 2 cm (Android) 0.27 0.20 A4). In the analysis phase of the second case study, we focused on the shaft of the statue; 3.2. Field Tests and Applications under Real Conditions therefore, the point cloud was appropriately sectioned in the middle of the shaft for anal- ysis. From th In thise an section, alysis we of the point c reported the data loud, analysis it was e of asy ﬁeld to detect tests and and performed isolate a dr under ift problem real and dynamic conditions (second, third and fourth case study). Examples of the scans of the for the iOS device during the scanning phase at a distance of 3 m. In Figure 11, the extent statue, the room and the rests of the Doric column are presented in Appendix B (Figure A4). of the drift can be estimated using the metric bar. The problem did not occur for the iOS In the analysis phase of the second case study, we focused on the shaft of the statue; and Android scans at 2 m; the sections of these latter scans are shown in Appendix B therefore, the point cloud was appropriately sectioned in the middle of the shaft for analysis. (Figure A5a,b). In Table 4, we reported the values of surface variation and omnivariance From the analysis of the point cloud, it was easy to detect and isolate a drift problem for obtained from the scans; the highest value of SVλ was associated with the scan where the the iOS device during the scanning phase at a distance of 3 m. In Figure 11, the extent drift problem occurred. Additionally, observing the omnivariance value, it can be seen of the drift can be estimated using the metric bar. The problem did not occur for the iOS that the inhomogeneity of the point cloud increases as the SVλ value increases. and Android scans at 2 m; the sections of these latter scans are shown in Appendix B (Figure A5a,b). In Table 4, we reported the values of surface variation and omnivariance Table 4. obtained Surfac from e variati the scans; on and the om highest nivariance value valu of SV es ofwas the associated statue, second with ca the se stu scan dywher . e the drift problem occurred. Additionally, observing the omnivariance value, it can be seen that Object Scanned Scan Distance: 2 m Scan Distance: 3 m the inhomogeneity of the point cloud increases as the SV value increases. SVλ 1.5 cm SVλ 2 cm SVλ 1.5 cm SVλ 2 cm 0.0071 0.0103 0.0417 No data Statue Oλ 1.5 cm Oλ 2 cm Oλ 1.5 cm Oλ 2 cm 0.0012 0.0013 0.0017 No data Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 12 of 22 The scan performed with the Android device at 3 m produced no results (it did not acquire any points); this indicates that the range of the device is less than 3 m. Figure 11b,c presents additional images of the point cloud affected by drift problems. Figure 11d shows Appl. Syst. Innov. 2022, 5, 63 12 of 22 a section (C-C) executed in the area of the scan closure, and Figure 11c shows a top view of the statue. (a) (b) (c) Figure 11. Sections of the point clouds of the statue scanned at distance of 3 m; the scalar field refers Figure 11. Sections of the point clouds of the statue scanned at distance of 3 m; the scalar ﬁeld refers to the surface variation value: section of stem of statue (a), vertical section C-C of the statue in the to the surface variation value: section of stem of statue (a), vertical section C-C of the statue in the scan closure zone (b), top view of the statue and indication of the vertical section C-C (c). scan closure zone (b), top view of the statue and indication of the vertical section C-C (c). Table 5 shows the surface variation and planarity values for the third case study. Table 4. Surface variation and omnivariance values of the statue, second case study. Figure 12 shows the room planimetry obtained by dissecting the point clouds at a height of approximately 1.50 m (the scalar field represents the planarity values). Looking at the Object Scanned Scan Distance: 2 m Scan Distance: 3 m planarity values in the table, it can be seen (see Figure 12) that the Android device per- SV 1.5 cm SV 2 cm SV 1.5 cm SV 2 cm formed unsatisfactorily for geomatics purposes. The iOS values, on the other hand, were 0.0071 0.0103 0.0417 No data quite encouraging for the scans performed at both 1 and 1.5 cm. Statue O 1.5 cm O 2 cm O 1.5 cm O 2 cm Table 5. Surface variation and planarity values of the point cloud of the lab. room, third case study. 0.0012 0.0013 0.0017 No data Object Scanned Scan Distance: Adaptative SVλ 1 cm SVλ 1.5 cm SVλ 2 cm The scan performed with the Android device at 3 m produced no results (it did not acquire any points); this indicates that the range of the device is less than 3 m. Figure 11b,c 0.0119 0.0109 0.0234 Laboratory room presents additional images of the point cloud affected by drift problems. Figure 11d shows Pλ 1 cm Pλ 1.5 cm Pλ 2 cm a section (C-C) executed in the area of the scan closure, and Figure 11c shows a top view of 0.8607 0.8940 0.7849 the statue. Table 5 shows the surface variation and planarity values for the third case study. Figure 12 shows the room planimetry obtained by dissecting the point clouds at a height of approximately 1.50 m (the scalar ﬁeld represents the planarity values). Looking at the planarity values in the table, it can be seen (see Figure 12) that the Android device performed unsatisfactorily for geomatics purposes. The iOS values, on the other hand, were quite encouraging for the scans performed at both 1 and 1.5 cm. Table 5. Surface variation and planarity values of the point cloud of the lab. room, third case study. Object Scanned Scan Distance: Adaptative (a) (b) (c) SV 1 cm SV 1.5 cm SV 2 cm Figure 12. Sections of the point clouds of the laboratory room; scalar field refers to the planarity 0.0119 0.0109 0.0234 Laboratory room value: 1 cm resolution scan (a), 1.5 cm resolution scan (b), 2 cm resolution scan (c). P 1 cm P 1.5 cm P 2 cm 0.8607 0.8940 0.7849 The fourth case study involved the scanning of the remains of a Doric column located in Taranto. We performed three scans with resolutions of 1 cm (iOS), 1.5 cm (iOS) and 2 cm (Android). In Table 6, we reported the surface variation and omnivariance values ob- tained for the three scans. Observing the values, it can be seen that the scan made at a resolution of 1.5 cm (iOS) was the worst. Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 12 of 22 The scan performed with the Android device at 3 m produced no results (it did not acquire any points); this indicates that the range of the device is less than 3 m. Figure 11b,c presents additional images of the point cloud affected by drift problems. Figure 11d shows a section (C-C) executed in the area of the scan closure, and Figure 11c shows a top view of the statue. (a) (b) (c) Figure 11. Sections of the point clouds of the statue scanned at distance of 3 m; the scalar field refers to the surface variation value: section of stem of statue (a), vertical section C-C of the statue in the scan closure zone (b), top view of the statue and indication of the vertical section C-C (c). Table 5 shows the surface variation and planarity values for the third case study. Figure 12 shows the room planimetry obtained by dissecting the point clouds at a height of approximately 1.50 m (the scalar field represents the planarity values). Looking at the planarity values in the table, it can be seen (see Figure 12) that the Android device per- formed unsatisfactorily for geomatics purposes. The iOS values, on the other hand, were quite encouraging for the scans performed at both 1 and 1.5 cm. Table 5. Surface variation and planarity values of the point cloud of the lab. room, third case study. Object Scanned Scan Distance: Adaptative SVλ 1 cm SVλ 1.5 cm SVλ 2 cm 0.0119 0.0109 0.0234 Laboratory room Appl. Syst. Innov. 2022, 5, 63 13 of 22 Pλ 1 cm Pλ 1.5 cm Pλ 2 cm 0.8607 0.8940 0.7849 (a) (b) (c) Figure 12. Sections of the point clouds of the laboratory room; scalar field refers to the planarity Figure 12. Sections of the point clouds of the laboratory room; scalar ﬁeld refers to the planarity value: 1 cm resolution scan (a), 1.5 cm resolution scan (b), 2 cm resolution scan (c). value: 1 cm resolution scan (a), 1.5 cm resolution scan (b), 2 cm resolution scan (c). The fourth case study involved the scanning of the remains of a Doric column located The fourth case study involved the scanning of the remains of a Doric column located in Taranto. We performed three scans with resolutions of 1 cm (iOS), 1.5 cm (iOS) and 2 in Taranto. We performed three scans with resolutions of 1 cm (iOS), 1.5 cm (iOS) and 2 cm cm (Android). In Table 6, we reported the surface variation and omnivariance values ob- Appl. Syst. Innov. 2022, 5, x FOR PEER RE(Andr VIEW oid). In Table 6, we reported the surface variation and omnivariance values obtained 13 of 22 tained for the three scans. Observing the values, it can be seen that the scan made at a for the three scans. Observing the values, it can be seen that the scan made at a resolution resolution of 1.5 cm (iOS) was the worst. of 1.5 cm (iOS) was the worst. Table 6. Surface variation and omnivariance values of the point cloud of the rests of the column, Table 6. Surface variation and omnivariance values of the point cloud of the rests of the column, fourth case study. fourth case study. Object Scanned Scan Distance: Adaptative Object Scanned Scan Distance: Adaptative SVλ 1 cm SVλ 1.5 cm SVλ 2 cm SV 1 cm SV 1.5 cm SV 2 cm 0.0400 0.0682 0.0309 0.0400 0.0682 0.0309 Doric column rests Doric column rests Oλ 1 cm Oλ 1.5 cm Oλ 2 cm O 1 cm O 1.5 cm O cm 0.0019 0.0022 0.0018 0.0019 0.0022 0.0018 In order to identify possible drift and splitting of the surface, we carried out sections In order to identify possible drift and splitting of the surface, we carried out sections of the point cloud in the vertical (sect. A-A) and horizontal (on the third column drum of the point cloud in the vertical (sect. A-A) and horizontal (on the third column drum sect. sect. B-B) scan closing zones (Figure 13). B-B) scan closing zones (Figure 13). (a) (b) Figure 13. Sections of the scan closure zones: section at the closure of the scan of the lateral surface Figure 13. Sections of the scan closure zones: section at the closure of the scan of the lateral surface (a), section at the vertical closure of the scan on the third column drum (b). (a), section at the vertical closure of the scan on the third column drum (b). Figure 14 presents the A-A and B-B sections carried out on the three point clouds. In Figure 14 presents the A-A and B-B sections carried out on the three point clouds. In particular, Figure 14a,d shows the sections taken on the 1 cm resolution scan, Figure 14b,e particular, Figure 14a,d shows the sections taken on the 1 cm resolution scan, Figure 14b,e shows the sections carried out on the 1.5 cm resolution scan and Figure 14c,f shows the shows the sections carried out on the 1.5 cm resolution scan and Figure 14c,f shows the sections carried out on the 2 cm resolution scan. sections carried out on the 2 cm resolution scan. (a) (b) (c) (d) (e) (f) Figure 14. Sections of the point cloud in the closure zones: vertical section (A-A) of the point cloud with resolution 1 cm (a), vertical section (A-A) of the point cloud with resolution 1.5 cm (b), vertical section (A-A) of the point cloud with resolution 2 cm (c), horizontal section (B-B) of the point cloud with resolution 1 cm (d), horizontal section (B-B) of the point cloud with resolution 1.5 cm (e), hori- zontal section (B-B) of the point cloud with resolution 2 cm (f). Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 13 of 22 Table 6. Surface variation and omnivariance values of the point cloud of the rests of the column, fourth case study. Object Scanned Scan Distance: Adaptative SVλ 1 cm SVλ 1.5 cm SVλ 2 cm 0.0400 0.0682 0.0309 Doric column rests Oλ 1 cm Oλ 1.5 cm Oλ 2 cm 0.0019 0.0022 0.0018 In order to identify possible drift and splitting of the surface, we carried out sections of the point cloud in the vertical (sect. A-A) and horizontal (on the third column drum sect. B-B) scan closing zones (Figure 13). (a) (b) Figure 13. Sections of the scan closure zones: section at the closure of the scan of the lateral surface (a), section at the vertical closure of the scan on the third column drum (b). Figure 14 presents the A-A and B-B sections carried out on the three point clouds. In particular, Figure 14a,d shows the sections taken on the 1 cm resolution scan, Figure 14b,e Appl. Syst. Innov. 2022, 5, 63 14 of 22 shows the sections carried out on the 1.5 cm resolution scan and Figure 14c,f shows the sections carried out on the 2 cm resolution scan. (a) (b) (c) (d) (e) (f) Figure 14. Sections of the point cloud in the closure zones: vertical section (A-A) of the point cloud Figure 14. Sections of the point cloud in the closure zones: vertical section (A-A) of the point cloud with resolution 1 cm (a), vertical section (A-A) of the point cloud with resolution 1.5 cm (b), vertical with resolution 1 cm (a), vertical section (A-A) of the point cloud with resolution 1.5 cm (b), vertical section (A-A) of the point cloud with resolution 2 cm (c), horizontal section (B-B) of the point cloud section (A-A) of the point cloud with resolution 2 cm (c), horizontal section (B-B) of the point cloud with resolution 1 cm (d), horizontal section (B-B) of the point cloud with resolution 1.5 cm (e), hori- with resolution 1 cm (d), horizontal section (B-B) of the point cloud with resolution 1.5 cm (e), zontal section (B-B) of the point cloud with resolution 2 cm (f). horizontal section (B-B) of the point cloud with resolution 2 cm (f). Observing the sections, it can be seen that the scan with a resolution of 1.5 cm (Figure 14b,e) was most affected by surface splitting related to drift problems. Surface splitting occurred in the horizontal direction when the operator scanned the side surface of the object and in the vertical direction when the operator scanned the column drum at the top of the column to close the scan. The 1 cm resolution scan (Figure 14a,d) showed minor surface splitting on the third column drum compared to the 1.5 cm resolution scan. The 2 cm resolution scan appears to be the best of the three; the scan only presented small problems at the junctions between one column drum and another. In addition, in order to assess the accuracy and precision of the point clouds obtained in the third and fourth case studies, the cloud-to-cloud (C2C) distance was calculated with respect to a point cloud surveyed with the TLS and photogrammetric method. In the third case study, the 3D point cloud was obtained using a HDS3000 Terrestrial Laser Scanner, which has a position accuracy of 6mm@50m. In the fourth case study, a photogrammetric survey using digital single lens reﬂex camera and a structure from motion–multi-view stereo (SfM-MVS) approach was per- formed [23]. In particular, a Nikon D3300 with a Nikkor 20 mm f/2.8D ﬁxed focal lens was used for the 3D survey [24]. The point cloud was built in an Agisoft Metashape environ- ment; the root mean square equivalent (RMSE), evaluated on six ground control points (GCPs), was 0.001 m. To compare the point clouds, it was necessary to subsample in order to obtain a statistically fair comparison. Table 7 shows the C2C values of mean and standard deviation of C2C distance for the third and fourth case studies between smartphone point clouds and reference point clouds; in addition, in Appendix B, we reported the histograms (Figure A6). Appl. Syst. Innov. 2022, 5, 63 15 of 22 Table 7. Mean and standard deviation values of C2C distance obtained in the third and fourth case studies. Resolution: 1 cm Resolution: 1.5 cm Resolution: 2 cm Object Scanned C2C [m] C2C [m] C2C [m] C2C [m] C2C [m] C2C [m] Laboratory room 0.0334 0.0264 0.0224 0.0449 0.0518 0.0580 Doric column rests 0.0153 0.0132 0.0383 0.0238 0.0127 0.0107 4. Discussion Scans performed under controlled laboratory conditions on materials in the group of plasters, plastics and frosted glass reported generally excellent SF and P values. This is a positive aspect considering that LiDAR smartphones, in their working conditions, may have to scan different material surfaces in the same scenario (e.g., a building, a square, a street, etc.). Observing the SF values in detail, there is a division between the “plasters” group and the “plastics and frosted glass” group, which have lower values than “plasters”. The difference is very small, and we estimate that this is not a problem for multimaterial scanning. The steel and brass samples scanned at a distance of 0.5 m and resolutions of 1 cm and 1.5 cm showed considerable noise to the extent that they could not be analysed. Scanning Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 15 of 22 the same samples at a resolution of 2 cm showed a surface variation value of 0.1672 and a cracked and deformed surface (Figure 15a). (a) (b) (c) Figure 15. Scans affected by aberrations; scalar field refers to the surface variation value: steel with Figure 15. Scans affected by aberrations; scalar ﬁeld refers to the surface variation value: steel with 2 cm resolution (a), brass with 1.5 cm resolution (b), brass with 2 cm resolution (c). 2 cm resolution (a), brass with 1.5 cm resolution (b), brass with 2 cm resolution (c). The planarity values for the Android device are interesting, as this device achieved Steel and brass samples scanned at a distance of 1.5 m showed generally better surface equal or better results than iOS devices with a lower resolution. The possible deformations variation values than scans performed at a closer distance (0.5 m). The 1 cm resolution scan of planar surfaces can be better evaluated only in tests performed under dynamic condi- of brass was good, while the 1.5 cm and 2 cm resolution scans produced surface variation tions. values of 0.1455 and 0.1000 due to some splitting and deformation (Figure 15b,c). Problems Analyses regarding a linear correlation between albedo and surface variation showed in scanning metallic materials could be caused by the surface structure; metallic materials a moderate to very weak relationship. This is another positive element since it indicates can be scanned more easily by increasing the acquisition distance and angle of the laser that the nature of the material does not primarily affect the quality of the scans. beam [15]. Through tests performed under dynamic conditions, we were able to highlight the The planarity values for the Android device are interesting, as this device achieved problems associated with moving scans. Two main problems emerged: the splitting of equal or better results than iOS devices with a lower resolution. The possible deformations surfaces and the incorrect closure of scans caused by inertial navigation system (INS) drift of planar surfaces can be better evaluated only in tests performed under dynamic conditions. problems. Drift problems depend on calculation of the smartphone’s position. During the Analyses regarding a linear correlation between albedo and surface variation showed scanning process, the position of the smartphone is determined instant by instant based a moderate to very weak relationship. This is another positive element since it indicates on the previous position estimated by inertial sensors. In this process, small errors of the that the nature of the material does not primarily affect the quality of the scans. INS caThr n prop ough agate an tests performed d cause large po undersitioning dynamic error conditions, s in the we scann wer ing o e able f larg to highlight e objects or the lar pr goblems e scenarassociated ios [13,25]. with However, t moving he scans. drift prob Twolem main s inpr pract oblems ice al emer so re ged: sulted thein t splitting he sur-of face surfaces s splitting and and overlapp the incorrect closur ing in the sc e of scans an closu caused re ar by eas. There inertial navigation fore, it can also be said th system (INS) drift at the two probl problems. Drift ems are, to problems a certa depend in degree, rel on calculation ated. of the smartphone’s position. During the scanning process, the position of the smartphone is determined instant by instant based Observing the second case study, it can be deduced that increasing the distance to on the previous position estimated by inertial sensors. In this process, small errors of the the object being scanned increases the possibility of encountering drift problems. This is verifiable in the case of scans performed on an object along a circular path; furthermore, varying the distance to the object being scanned (e.g., due to obstacles) increases the pos- sibility of the occurrence of a drift effect. The phenomenon just described can also be ob- served in the scans of two structures collected for documentation purposes. Both struc- tures were scanned with the iPAD Pro 2021 with a resolution of 1.5 cm, as shown in Figure (a) (b) Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 15 of 22 (a) (b) (c) Figure 15. Scans affected by aberrations; scalar field refers to the surface variation value: steel with 2 cm resolution (a), brass with 1.5 cm resolution (b), brass with 2 cm resolution (c). The planarity values for the Android device are interesting, as this device achieved equal or better results than iOS devices with a lower resolution. The possible deformations of planar surfaces can be better evaluated only in tests performed under dynamic condi- tions. Analyses regarding a linear correlation between albedo and surface variation showed a moderate to very weak relationship. This is another positive element since it indicates that the nature of the material does not primarily affect the quality of the scans. Through tests performed under dynamic conditions, we were able to highlight the problems associated with moving scans. Two main problems emerged: the splitting of surfaces and the incorrect closure of scans caused by inertial navigation system (INS) drift problems. Drift problems depend on calculation of the smartphone’s position. During the scanning process, the position of the smartphone is determined instant by instant based Appl. Syst. Innov. 2022, 5, 63 16 of 22 on the previous position estimated by inertial sensors. In this process, small errors of the INS can propagate and cause large positioning errors in the scanning of large objects or large scenarios [13,25]. However, the drift problems in practice also resulted in the sur- INS can propagate and cause large positioning errors in the scanning of large objects or faces splitting and overlapping in the scan closure areas. Therefore, it can also be said that large scenarios [13,25]. However, the drift problems in practice also resulted in the surfaces the two problems are, to a certain degree, related. splitting and overlapping in the scan closure areas. Therefore, it can also be said that the two Observing problems the secon are, to a certain d cas degr e stud ee, y, related. it can be deduced that increasing the distance to Observing the second case study, it can be deduced that increasing the distance to the object being scanned increases the possibility of encountering drift problems. This is the object being scanned increases the possibility of encountering drift problems. This is verifiable in the case of scans performed on an object along a circular path; furthermore, veriﬁable in the case of scans performed on an object along a circular path; furthermore, varying the distance to the object being scanned (e.g., due to obstacles) increases the pos- varying the distance to the object being scanned (e.g., due to obstacles) increases the sibility of the occurrence of a drift effect. The phenomenon just described can also be ob- possibility of the occurrence of a drift effect. The phenomenon just described can also served in the scans of two structures collected for documentation purposes. Both struc- be observed in the scans of two structures collected for documentation purposes. Both tures were scanned with the iPAD Pro 2021 with a resolution of 1.5 cm, as shown in Figure structures were scanned with the iPAD Pro 2021 with a resolution of 1.5 cm, as shown in Figure 16. Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 16 of 22 (a) (b) (c) (d) Figure 16. Scans of two structures collected for documentary purposes; scalar field refers to the sur- Figure 16. Scans of two structures collected for documentary purposes; scalar ﬁeld refers to the face variation value: first structure (a), second structure (b), section of first structure (c), section of surface variation value: ﬁrst structure (a), second structure (b), section of ﬁrst structure (c), section of second structure (d). second structure (d). In the ﬁrst structure (Figure 16a), due to tight spaces, the operator scanned by standing In the first structure (Figure 16a), due to tight spaces, the operator scanned by stand- still in the centre of the structure and rotating in place. In the second structure (Figure 16b), ing still in the centre of the structure and rotating in place. In the second structure (Figure the operator moved actively inside the structure to collect every detail. The scans of the 16b), the operator moved actively inside the structure to collect every detail. The scans of structures are generally good, but when analysing the sections, it is evident that the section the structures are generally good, but when analysing the sections, it is evident that the of the ﬁrst structure (Figure 16c) has fewer problems (or no problems) with surface splitting section of the first structure (Figure 16c) has fewer problems (or no problems) with surface compared to the second (Figure 16d), which has surface splitting at the entrance to the splitting compared to the second (Figure 16d), which has surface splitting at the entrance to the structure, where the scan closed. This is an example of how movement can generate drift problems that practically result in surface splitting. Finally, through the third and fourth case studies, it can be seen that in general, the accuracies are on the order of 1–3 cm, but scanning problems such as surface splitting can degrade these values to more than double their original values (see Table 7). For these reasons, it is very important to monitor these issues in scans performed with smartphones and the analysis methods described in this paper. 5. Conclusions In this research, we evaluated and compared the performance of depth sensors mounted on iOS (iPhone 12 Pro and iPad 2021 Pro) and Android (Huawei P30 Pro) devices based on five case studies. In addition, we related mathematical descriptors to specific smartphone point cloud issues in order to quantify anomalies occurring in scans to better understand the point cloud and to simplify visual analysis using scalar fields. This method also allows the evaluation and identification of issues that may affect mobile device scans without additional reference scans that are not available in real applications. In the cases studied, we applied the eigenfeatures of surface variation, planarity and omnivariation; in the case of long and thin structures (e.g., pipes, traffic signals, streetlights etc.), the ei- genfeature of linearity should be used. Generally, mobile devices have proven to be useful tools for scanning objects and environments in urban scenarios; their main benefits are: light weight, manageability, the possibility of having a scanning tool with you at all times, the possibility of quickly shar- ing the models created with other users, the speed of scanning and the possibility of check- ing scans directly on site and rescanning them if necessary. The problems encountered can be attributed to the occurrence of certain anomalies, such as surface splitting, loss of flatness and drift problems in the INS. Another problem is the low diffusion of these sen- sors on smartphones. Currently, only a few devices are equipped with depth sensors. This is related to market demands, so there may be an evolution of these in the coming years. Observing the third and fourth case studies, we can deduce that the precisions achievable by these sensors are around 1–3 cm in the absence of anomalies. The possible Appl. Syst. Innov. 2022, 5, 63 17 of 22 structure, where the scan closed. This is an example of how movement can generate drift problems that practically result in surface splitting. Finally, through the third and fourth case studies, it can be seen that in general, the accuracies are on the order of 1–3 cm, but scanning problems such as surface splitting can degrade these values to more than double their original values (see Table 7). For these reasons, it is very important to monitor these issues in scans performed with smartphones and the analysis methods described in this paper. 5. Conclusions In this research, we evaluated and compared the performance of depth sensors mounted on iOS (iPhone 12 Pro and iPad 2021 Pro) and Android (Huawei P30 Pro) devices based on ﬁve case studies. In addition, we related mathematical descriptors to speciﬁc smartphone point cloud issues in order to quantify anomalies occurring in scans to better understand the point cloud and to simplify visual analysis using scalar ﬁelds. This method also allows the evaluation and identiﬁcation of issues that may affect mobile device scans without additional reference scans that are not available in real applications. In the cases studied, we applied the eigenfeatures of surface variation, planarity and omnivariation; in the case of long and thin structures (e.g., pipes, trafﬁc signals, streetlights etc.), the eigenfeature of linearity should be used. Generally, mobile devices have proven to be useful tools for scanning objects and environments in urban scenarios; their main beneﬁts are: light weight, manageability, the possibility of having a scanning tool with you at all times, the possibility of quickly sharing the models created with other users, the speed of scanning and the possibility of checking scans directly on site and rescanning them if necessary. The problems encountered can be attributed to the occurrence of certain anomalies, such as surface splitting, loss of ﬂatness and drift problems in the INS. Another problem is the low diffusion of these sensors on smartphones. Currently, only a few devices are equipped with depth sensors. This is related to market demands, so there may be an evolution of these in the coming years. Observing the third and fourth case studies, we can deduce that the precisions achiev- able by these sensors are around 1–3 cm in the absence of anomalies. The possible ﬁelds of application concern the scanning of small- and medium-sized objects and scenarios, compatible with the movement of a human being, for subsequent 3D modelling. In this paper, particular insight was applied to some freeform structures; further applications of 3D modelling can be found in Wang et al. [26]. In addition, large objects and scenarios were not considered; therefore, subsequent research will focus on the acquisition of large structures and new apps capable of handling complex scenarios. Author Contributions: Conceptualization, D.C., G.V., M.P. and V.S.A.; methodology, G.V., D.C., M.P. and V.S.A.; software, G.V., D.C., M.P. and V.S.A.; validation, G.V., D.C., M.P. and V.S.A.; formal analysis, G.V., D.C., M.P. and V.S.A.; data curation, G.V., D.C., M.P. and V.S.A.; writing—original draft preparation, G.V., D.C., M.P. and V.S.A.; writing—review and editing, M.P., D.C., G.V. and V.S.A.; visualization, G.V., D.C., M.P. and V.S.A.; supervision, D.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A In Figure A1, we reported some examples of point clouds acquired with 1 cm resolution at a distance of 0.5 m of the following sample materials: raw cement plaster, white lime plaster and coloured lime plaster. Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 17 of 22 fields of application concern the scanning of small- and medium-sized objects and scenar- ios, compatible with the movement of a human being, for subsequent 3D modelling. In this paper, particular insight was applied to some freeform structures; further ap- plications of 3D modelling can be found in Wang et al. [26]. In addition, large objects and scenarios were not considered; therefore, subsequent research will focus on the acquisi- tion of large structures and new apps capable of handling complex scenarios. Author Contributions: Conceptualization, D.C., G.V., M.P. and V.S.A.; methodology, G.V., D.C., M.P. and V.S.A.; software, G.V., D.C., M.P. and V.S.A.; validation, G.V., D.C., M.P. and V.S.A.; for- mal analysis, G.V., D.C., M.P. and V.S.A.; data curation, G.V., D.C., M.P. and V.S.A.; writing—orig- inal draft preparation, G.V., D.C., M.P. and V.S.A.; writing—review and editing, M.P., D.C., G.V. and V.S.A.; visualization, G.V., D.C., M.P. and V.S.A.; supervision, D.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Appendix A In Figure A1, we reported some examples of point clouds acquired with 1 cm resolu- Appl. Syst. Innov. 2022, 5, 63 18 of 22 tion at a distance of 0.5 m of the following sample materials: raw cement plaster, white lime plaster and coloured lime plaster. (a) (b) (c) Figure A1. Examples of point clouds: raw cement plaster (a), white lime plaster (b), coloured lime Figure A1. Examples of point clouds: raw cement plaster (a), white lime plaster (b), coloured lime plaster (c). plaster (c). In Table A1, we reported the mean surface variation values obtained for the scans In Table A1, we reported the mean surface variation values obtained for the scans performed at 0.5 m and 1.5 m with resolutions of 1 cm, 1.5 cm and 2 cm. The value “No performed at 0.5 m and 1.5 m with resolutions of 1 cm, 1.5 cm and 2 cm. The value “No data” was assigned to the scans that were so compromised that they could not be analysed. data” was assigned to the scans that were so compromised that they could not be ana- In Figure A2, we reported the surface variation values calculated for the samples scanned lysed. In Figure A2, we reported the surface variation values calculated for the samples at 1.5 m. scanned at 1.5 m. Table A1. Mean surface variation values of the scans of the 10 samples. Scan Distance: 0.5 m Scan Distance: 1.5 m Sample Material SV 1 cm SV 1.5 cm SV 2 cm SV 1 cm SV 1.5 cm SV 2 cm Smooth cement plaster 0.0020 0.0041 0.0082 0.0016 0.0041 0.0060 Raw cement plaster 0.0024 0.0037 0.0056 0.0016 0.0019 0.0036 White lime plaster 0.0021 0.0022 0.0037 0.0005 0.0013 0.0017 Coloured lime plaster 0.0009 0.0026 0.0021 0.0004 0.0010 0.0008 Tetraﬂuoroethylene (TFE) 0.0013 0.0034 0.0004 0.0001 0.0001 0.0003 Methacrylate (PMMA) 0.0012 0.0010 0.0003 0.0002 0.0002 0.0003 High-density polyethylene (HDPE) 0.0003 0.0001 0.0001 0.0001 0.0001 0.0002 Frosted glass 0.0004 0.0001 0.0001 0.0002 0.0001 0.0001 Steel No data No data 0.1672 0.0005 0.0003 0.0051 Brass 0.0021 0.1455 0.1000 0.0003 0.0007 0.0142 In Table A2, we reported the mean planarity values obtained for the scans taken at 0.5 m and 1.5 m with resolutions of 1 cm, 1.5 cm and 2 cm. The omitted “-” values result from visibly deformed surfaces (e.g., Figure 10) where a planarity analysis was not possible. In Figure A3, we reported the calculated planarity values for the samples scanned at a distance of 1.5 m. Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 18 of 22 Table A1. Mean surface variation values of the scans of the 10 samples. Scan Distance: 0.5 Meter Scan Distance: 1.5 Meter Sample Material SVλ 1 cm SVλ 1.5 cm SVλ 2 cm SVλ 1 cm SVλ 1.5 cm SVλ 2 cm Smooth cement plaster 0.0020 0.0041 0.0082 0.0016 0.0041 0.0060 Raw cement plaster 0.0024 0.0037 0.0056 0.0016 0.0019 0.0036 White lime plaster 0.0021 0.0022 0.0037 0.0005 0.0013 0.0017 Coloured lime plaster 0.0009 0.0026 0.0021 0.0004 0.0010 0.0008 Tetrafluoroethylene (TFE) 0.0013 0.0034 0.0004 0.0001 0.0001 0.0003 Methacrylate (PMMA) 0.0012 0.0010 0.0003 0.0002 0.0002 0.0003 High-density polyethylene (HDPE) 0.0003 0.0001 0.0001 0.0001 0.0001 0.0002 Frosted glass 0.0004 0.0001 0.0001 0.0002 0.0001 0.0001 Appl. Syst. Innov. 2022, 5, 63 19 of 22 Steel No data No data 0.1672 0.0005 0.0003 0.0051 Brass 0.0021 0.1455 0.1000 0.0003 0.0007 0.0142 Figure A2. Comparison of mean values of surface variation for samples scanned at a distance of 1.5 Figure A2. Comparison of mean values of surface variation for samples scanned at a distance of m. 1.5 m. Table A2. Mean Planarity values of the scans of the 10 samples. In Table A2, we reported the mean planarity values obtained for the scans taken at 0.5 m and 1.5 m with resolutions of 1 cm, 1.5 cm and 2 cm. The omitted “-” values result Scan Distance: 0.5 m Scan Distance: 1.5 m Sample Material from visibly deformed surfaces (e.g., Figure 10) where a planarity analysis was not possi- P 1 cm P 1.5 cm P 2 cm P 1 cm P 1.5 cm P 2 cm ble. In Figure A3, we reported the calculated planarity values for the samples scanned at Smooth cement plaster 0.70 0.53 0.73 0.66 0.52 0.67 a distance of 1.5 m. Raw cement plaster 0.70 0.54 0.71 0.65 0.53 0.68 White lime plaster 0.69 0.53 0.74 0.67 0.52 0.78 Coloured lime plaster 0.71 0.53 0.80 0.67 0.52 0.84 Table A2. Mean Planarity values of the scans of the 10 samples. Tetraﬂuoroethylene (TFE) 0.67 0.50 0.72 0.65 0.52 0.70 Methacrylate (PMMA) 0.66 0.51 0.63 0.65 0.48 0.73 Scan Distance: 0.5 Meter Scan Distance: 1.5 Meter Sample Material High-density polyethylene (HDPE) 0.67 0.51 0.76 0.66 0.51 0.75 Pλ 1 cm Pλ 1.5 cm Pλ 2 cm Pλ 1 cm Pλ 1.5 cm Pλ 2 cm Frosted glass 0.70 0.54 0.82 0.67 0.52 0.79 Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 19 of 22 Smooth cement plaster Steel 0.70 No0.53 data No0. data73 0. -66 0. 0.6752 0. 0.5267 0.66 Brass 0.63 - - 0.63 0.53 - Raw cement plaster 0.70 0.54 0.71 0.65 0.53 0.68 White lime plaster 0.69 0.53 0.74 0.67 0.52 0.78 Coloured lime plaster 0.71 0.53 0.80 0.67 0.52 0.84 Tetrafluoroethylene (TFE) 0.67 0.50 0.72 0.65 0.52 0.70 Methacrylate (PMMA) 0.66 0.51 0.63 0.65 0.48 0.73 High-density polyethylene (HDPE) 0.67 0.51 0.76 0.66 0.51 0.75 Frosted glass 0.70 0.54 0.82 0.67 0.52 0.79 Steel No data No data - 0.67 0.52 0.66 Brass 0.63 - - 0.63 0.53 - Figure A3. Comparison of m Figure A3.eComparison an values of p of mean lanarity fo values r sa of mples scanned planarity for samples at a distance of scanned at a 1.5 distance m. of 1.5 m. Appendix B In Figure A4, we showed some examples of the scans acquired with different resolu- tions of the statue, the room and the remains of the Doric column. In Figure A4a, we re- ported the scan of the statue performed with a resolution of 1.5 cm; in Figure A4b, the scan of the laboratory room was performed with a resolution of 1 cm; in Figure A4c, the scan of the remains of the Doric column with a resolution of 1 cm; in Figure A4d, the scan of the remains of the Doric column with a resolution of 1.5 cm and in Figure A4e, the scan of the remains of the Doric column with a resolution of 2 cm. (a) (b) (c) (d) (e) Figure A4. Examples of scans acquired in the second, third, fourth and fifth case studies: statue (a), room (b), the remains of a Doric column (c–e). Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 19 of 22 Appl. Syst. Innov. 2022, 5, 63 20 of 22 Figure A3. Comparison of mean values of planarity for samples scanned at a distance of 1.5 m. Appendix B Appendix B In Figure A4, we showed some examples of the scans acquired with different resolu- In Figure A4, we showed some examples of the scans acquired with different reso- tions of the statue, the room and the remains of the Doric column. In Figure A4a, we re- lutions of the statue, the room and the remains of the Doric column. In Figure A4a, we ported the scan of the statue performed with a resolution of 1.5 cm; in Figure A4b, the reported the scan of the statue performed with a resolution of 1.5 cm; in Figure A4b, the scan of the laboratory room was performed with a resolution of 1 cm; in Figure A4c, the scan of the laboratory room was performed with a resolution of 1 cm; in Figure A4c, the scan of the remains of the Doric column with a resolution of 1 cm; in Figure A4d, the scan scan of the remains of the Doric column with a resolution of 1 cm; in Figure A4d, the scan of the remains of the Doric column with a resolution of 1.5 cm and in Figure A4e, the scan of the remains of the Doric column with a resolution of 1.5 cm and in Figure A4e, the scan of the remains of the Doric column with a resolution of 2 cm. of the remains of the Doric column with a resolution of 2 cm. (a) (b) (c) (d) (e) Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 20 of 22 Figure A4. Examples of scans acquired in the second, third, fourth and fifth case studies: statue (a), Figure A4. Examples of scans acquired in the second, third, fourth and ﬁfth case studies: statue (a), room (b), the remains of a Doric column (c–e). room (b), the remains of a Doric column (c–e). Figure A5 shows sections of the stem of the statue performed at a distance of 2 m. In Figure A5 shows sections of the stem of the statue performed at a distance of 2 m. In particular, Figure A5a shows the section of the statue stem scanned with a resolution of particular, Figure A5a shows the section of the statue stem scanned with a resolution of 1.5 cm and Figure A5b shows the section of the scan with a resolution of 2 cm. 1.5 cm and Figure A5b shows the section of the scan with a resolution of 2 cm. (a) (b) Figure A5. Sections of the point clouds of the stem of the statue; the scalar field refers to the surface Figure A5. Sections of the point clouds of the stem of the statue; the scalar ﬁeld refers to the surface variation value: 1.5 cm resolution scan performed at 2 m (a), 2 cm resolution scan performed at 2 m variation value: 1.5 cm resolution scan performed at 2 m (a), 2 cm resolution scan performed at (b). 2 m (b). Figure A6 shows the histograms of the calculated C2C distance for the third and fourth case studies. Figure A6a shows the histogram of the laboratory room scanned with 1 cm resolution; Figure A6b shows the histogram of the laboratory room scanned with 1.5 cm resolution; Figure A6c shows the histogram of the laboratory room scanned with 2 cm resolution. Figure A6d shows the histogram of the Doric column rests scanned at 1 cm resolution, Figure A6e shows the histogram of the Doric column rests scanned at 1.5 cm resolution, and Figure A6f shows the histogram of the Doric column rests scanned at 2 cm resolution. (a) (b) (c) (d) (e) (f) Figure A6. C2C histograms between smartphone point clouds and reference point clouds: labora- tory room point cloud with 1 cm resolution (a), 1.5 cm resolution (b) and 2 cm resolution (c); Doric column remains point cloud with 1 cm resolution (d), 1.5 cm resolution (e) and 2 cm resolution (f). Appl. Syst. Innov. 2022, 5, x FOR PEER REVIEW 20 of 22 Figure A5 shows sections of the stem of the statue performed at a distance of 2 m. In particular, Figure A5a shows the section of the statue stem scanned with a resolution of 1.5 cm and Figure A5b shows the section of the scan with a resolution of 2 cm. (a) (b) Figure A5. Sections of the point clouds of the stem of the statue; the scalar field refers to the surface Appl. Syst. Innov. 2022, 5, 63 21 of 22 variation value: 1.5 cm resolution scan performed at 2 m (a), 2 cm resolution scan performed at 2 m (b). Figure A6 shows the histograms of the calculated C2C distance for the third and Figure A6 shows the histograms of the calculated C2C distance for the third and fourth case studies. Figure A6a shows the histogram of the laboratory room scanned with fourth case studies. Figure A6a shows the histogram of the laboratory room scanned with 1 cm resolution; Figure A6b shows the histogram of the laboratory room scanned with 1.5 1 cm resolution; Figure A6b shows the histogram of the laboratory room scanned with cm resolution; Figure A6c shows the histogram of the laboratory room scanned with 2 cm 1.5 cm resolution; Figure A6c shows the histogram of the laboratory room scanned with resolution. Figure A6d shows the histogram of the Doric column rests scanned at 1 cm 2 cm resolution. Figure A6d shows the histogram of the Doric column rests scanned at resolution, Figure A6e shows the histogram of the Doric column rests scanned at 1.5 cm 1 cm resolution, Figure A6e shows the histogram of the Doric column rests scanned at resolution, and Figure A6f shows the histogram of the Doric column rests scanned at 2 cm 1.5 cm resolution, and Figure A6f shows the histogram of the Doric column rests scanned resolution. at 2 cm resolution. (a) (b) (c) (d) (e) (f) Figure A6. C2C histograms between smartphone point clouds and reference point clouds: labora- Figure A6. C2C histograms between smartphone point clouds and reference point clouds: laboratory tory room point cloud with 1 cm resolution (a), 1.5 cm resolution (b) and 2 cm resolution (c); Doric room point cloud with 1 cm resolution (a), 1.5 cm resolution (b) and 2 cm resolution (c); Doric column column remains point cloud with 1 cm resolution (d), 1.5 cm resolution (e) and 2 cm resolution (f). remains point cloud with 1 cm resolution (d), 1.5 cm resolution (e) and 2 cm resolution (f). References 1. Mikita, T.; Balková, M.; Bajer, A.; Cibulka, M.; Patocka, ˇ Z. Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops. Sensors 2020, 20, 1663. [CrossRef] [PubMed] 2. Luetzenburg, G.; Kroon, A.; Bjørk, A.A. Evaluation of the Apple IPhone 12 Pro LiDAR for an Application in Geosciences. Sci. Rep. 2021, 11, 22221. [CrossRef] 3. Gollob, C.; Ritter, T.; Kraßnitzer, R.; Tockner, A.; Nothdurft, A. 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Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

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ISSN: 2571-5577
DOI: 10.3390/asi5040063
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Abstract

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Applied System Innovation – Multidisciplinary Digital Publishing Institute

Published: Jun 29, 2022

Keywords: smartphone; LiDAR; point cloud analysis; ToF; Android; iOS

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Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

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Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

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