Color models in the process of 3D digitization of an artwork for presentation in a VR environment of an art gallery †

Ci a ion: D o o a, I.; Adamek, M. Colo Models in he P ocess o 3D Digi iza ion o an A wo k o P esen a ion in a VR En i onmen o an A Galle y. Elec onics 2024,13, 4431. h ps://doi.o g/10.3390/ elec onics13224431 Academic Edi o : Sil ia Libe a a Ullo Recei ed: 2 Oc obe 2024 Re ised: 1 No embe 2024 Accep ed: 5 No embe 2024 Published: 12 No embe 2024 Copy igh : © 2024 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). A icle Colo Models in he P ocess o 3D Digi iza ion o an A wo k o P esen a ion in a VR En i onmen o an A Galle y † I ena D o o a * and Milan Adamek Facul y o Applied In o ma ics, Tomas Ba a Uni e si y in Zlín, 760 05 Zlín, Czech Republic *Co espondence: d [email p o ec ed] †This pape is an ex ended e sion o ou pape published in Analysis o Na u al Ligh ing Condi ion o he Digi iza ion i A wo k in an A Galle y In e io . In P oceedings o he WSCG 2024 In e na ional Con e ence in Cen al Eu ope on Compu e G aphics, Visualiza ion and Compu e Vision, Compu e Science Resea ch No es, P ague, Czech Republic, 3–6 June 2024; pp. 391–394. h ps://doi.o g/10.24132/CSRN.3401.43. Abs ac : This s udy deals wi h he colo ep oduc ion o a wo k o a o digi ize i in o a 3D ealis ic model. The expe imen aims o digi ize a wo k o a o applica ion in a i ual eali y en i onmen conce ning ai h ul colo ep oduc ion. Pho og amme y and scanning wi h a LiDAR senso a e used o compa e he me hods and wo k wi h colo s du ing he econs uc ion o he 3D model. An inno a i e able wi h a came a and LiDAR senso is used o bo h me hods. A he same ime, cu en indings om he ield o colo ision and colo ime y a e applied o 3D econs uc ion. The expe imen ocuses on wo king wi h he RGB and L*a*b* colo models and, simul aneously, on he sRGB, CIE XYZ, and Rec.2020(HDR) colo spaces o ans o ming colo s in o a i ual en i onmen . Fo his pu pose, he colo is de ined in he Hex Colo Value o ma . This expe imen is a s a ing poin o u he esea ch on colo ep oduc ion in he digi al en i onmen . This s udy ep esen s a pa ial con ibu ion o he much-discussed a ea o o ge ies o wo ks o a in cu en ends in o ensics and o ge y. Keywo ds: colo model; image p ocessing; i ual eali y; pho og amme y; 3D model; 3D econs uc ion; o ensic science 1. In oduc ion Cu en ly, digi iza ion p ocesses a e e lec ed in all a eas o human ac i i y. Digi al echnologies a e used ac oss comme cial, scien i ic, and a is ic ields. Especially in he ield o a , emphasis is o en placed on he highly ealis ic quali y o digi al ep oduc- ion. Digi al echnologies a e al eady a s anda d pa o cap u ing and p ocessing images. Howe e , digi al echnology and image p ocessing p ocesses a e p ojec ed and pa o a is ic c ea ion [ 1 ]. Ne e heless, he digi al ep oduc ion o wo ks o a is s ill excellen o applying new p ocedu es, such as machine lea ning, especially in connec ion wi h new ends, such as 3D isualiza ion and i ual p esen a ions in he online en i onmen [2,3]. 3D ealis ic digi al ep oduc ion o a wo k o a also b ings many challenges and unsol ed issues in image p ocessing. This also depends on he digi iza ion i sel in 2D [ 4 ]. One o hem is he high quali y o he ep oduc ion o he objec in connec ion wi h i s ex u e and colo , which a e o en changeable due o he in luence o ligh and wea he condi ions. This p oblem is no ed especially in ex e io exhibi ions and a chi ec u e [ 5 ]. The echnology and me hodology chosen o he 3D digi iza ion p ocess also g ea ly in luence he quali y o he digi iza ion o he objec [ 6 ]. The me hod and p ocess o digi iza ion depend mainly on he inal ou pu o he digi ized objec . The eam can be 2D and 3D p in ing, 3D online p esen a ions, o using an objec in a i ual and augmen ed eali y (VR/AV) en i onmen in in e ac ion wi h he use [ 7 , 8 ]. Fo a ealis ic digi al 3D ep oduc ion o a wo k o a , g ea emphasis is placed on he quali y o he digi al image and i s ealism. Image ep oduc ion aims o ob ain as close as possible o he o iginal Elec onics 2024,13, 4431. h ps://doi.o g/10.3390/elec onics13224431 h ps://www.mdpi.com/jou nal/elec onics Elec onics 2024,13, 4431 2 o 15 image [ 9 ]. The same a ibu e o e alua ing ep oduc ion quali y is colo , which is di ec ly ela ed o ligh and human ision. This issue is deal wi h by colo ime y, colo , and human ision [10–12]. This ex esponds o cu en ends in he digi iza ion o a and he issue o ealis ic digi al ep oduc ion using he pho og amme y me hod and signi ican ly expands he indings p esen ed in a sho pape p esen ed a an in e na ional con e ence in Pilsen, Czech Republic [ 13 , 14 ]. The wo k also conside s using LiDAR (Ligh De ec ion And Ranging) senso s o apid image cap u e using a mobile applica ion in a mobile de ice [ 15 , 16 ]. The p esen ed expe imen aims o de e mine he ex en o which ambien ligh ing condi ions can in luence he 3D digi iza ion o a wo k o a in connec ion wi h he chosen modeling me hod. P e ious esea ch in he ield o image digi iza ion used sensing de ices, such as compac and DSLR (Digi al Single-Lens Re lex) came as, mobile de ice came as, lase scanne s, and 360◦came as. One sensing de ice is used in his expe imen [17–20]. The ollowing chap e s desc ibe he digi iza ion o he a wo k using he S M (S uc- u e om Mo ion) pho og amme y me hod and scanning wi h a LiDAR senso in he in e io . A he same ime, image cap u e is pe o med indoo s in dayligh . The in luence o ligh ing condi ions on he colo ep oduc ion o he 3D model is analyzed in 3D poin cloud models o one p ecisely de ined colo #758605 (Hex Colo Value), colo model RGB (117, 134, 5), and CIELAB/L*a*b* (54.30, − 8.46, 3.83). The indings om his expe imen will subsequen ly be used o analyze he colo isualiza ion o ealis ic 3D digi al ep oduc ion o a wo k ypes in a VR en i onmen . Cu en s udies on colo ep oduc ion in a is ic pain ings do no ocus on di ec , ealis ic colo ep oduc ion based on a p ecisely de ined colo o a se o colo s measu ed on an objec [21–23]. The e o e, wo king wi h colo s and colo models is a sui able solu ion o he ealis ic ep oduc ion o wo ks o a in digi al and i ual en i onmen s. Accu a e colo ep oduc- ion and wo king wi h he colo ligh spec um can be a sui able complemen a y p ocess o de e mining he o iginal colo in a digi al en i onmen . The ma ke o duplica es, no only in he ield o a bu also in he clo hing and indus ial sec o s, ep esen s a la ge a ea o he ade in coun e ei s [ 24 , 25 ]. Fo ensic science is also used he e, mainly in digi al o ensic a , he subjec o which is also he 2D and 3D ealis ic ep oduc ion o objec s [ 26 , 27 ]. In he digi al ep oduc ion o wo ks o a , he colo and ex u e o he ma e ial play impo an oles. The ype and na u e o ligh ing also a ec hese wo a ibu es. This expe imen ep esen s he i s basic esea ch in he ield o ealis ic colo ep oduc ion in a i ual en i onmen wi h he aim o applica ion in c iminology and o ensic in es iga ion, which will complemen he cu en me hods applied in o ensic sciences. 2. Ma e ials and Me hods The de elopmen o digi al echnologies and sensing de ices, which g adually e- placed he analog me hod o image p ocessing, image digi iza ion p ocesses, and g aphic so wa e o image p ocessing acco ding o he ype and pu pose o he inal ou pu , has also been de eloped and imp o ed. This includes de eloping and digi izing p in , digi al, and 3D p in ing, as well as 2D and 3D online p esen a ions [ 25 , 28 ]. Cu en ly, i ual and augmen ed eali y (VR/AR) echnologies a e also being pe ec ed and a e now a ailable o he gene al public, especially in he gaming indus y [ 16 , 29 – 31 ]. The ollowing sec ion desc ibes he digi iza ion o he a wo k. Sec ions 2.3 and 2.4 desc ibe image digi iza ion using he S M (S uc u e o Mo ion) pho og amme y me hod and LiDAR senso scanning o c ea e a ealis ic 3D digi al model. 2.1. Digi iza ion o A : A Pain ing wi h Ac ylic Pain s on Can as The a is ic objec o 3D digi al ep oduc ion was pain ed wi h ac ylic pain s on he can as. The a wo k is domina ed by g een and b own ac ylic pain s, and he ones o hese wo colo s a e c ea ed by mixing hese ac ylic pain s. As seen in Figu e 1a. Also ma ked in ed in his igu e is he base colo space. In his place, he colo alue was measu ed wi h a Colo ca ch NANO colo ime e om he Swiss company Colo ix SA Elec onics 2024,13, 4431 3 o 15 (Neuchâ el, Swi ze land), and he alue o he di ec g een colo RGB = 117, 134, 123, and L*a*b = 54.30 , − 8.46, 3.83 [ 32 ]. These alues p o ed su icien o con e he colo alue in o a Hex = #758605 colo model o subsequen colo segmen a ion in a ealis ic 3D poin cloud model o he objec . The CIE Lab/L*a*b* colo space model is applied o digi al imaging ac oss digi al and display de ices. Elec onics 2024, 13, x FOR PEER REVIEW 3 o 16 wo colo s a e c ea ed by mixing hese ac ylic pain s. As seen in Figu e 1a. Also ma ked in ed in his igu e is he base colo space. In his place, he colo alue was measu ed wi h a Colo ca ch NANO colo ime e om he Swiss company Colo ix SA (Neuchâ el, Swi ze land), and he alue o he di ec g een colo RGB = 117, 134, 123, and L*a*b = 54.30, −8.46, 3.83 [32]. These alues p o ed su icien o con e he colo alue in o a Hex = #758605 colo model o subsequen colo segmen a ion in a ealis ic 3D poin cloud model o he objec . The CIE Lab/L*a*b* colo space model is applied o digi al imaging ac oss digi al and display de ices. (a) (b) (c) Figu e 1. Digi iza ion o an a objec : (a) 2D digi ized objec and de ail ma ked in ed; (b) ma ix o pa ial de ails he yellow ange o he image; and (c) isualiza ion o he de ail o he s uc u e and colo o a pa ial pa o he objec . Figu e 1a shows a pho og aph o a digi ized objec wi h a ma ked space in which he alue o he di ec g een colo RGB = 117, 134, 123 was measu ed by he Colo ca ch NANO colo ime e . Figu e 1b shows a de ailed ma ix o he colo s uc u e. The link ma ix is ma ked in he yellow ange o 5.563 µm × 4.171 µm. Figu e 1b shows he de ails o he ma e ial s uc u e o he objec o he ma ked elemen o he ma ix shown in Figu e 1c. This essen ial isual inspec ion o he colo o he objec in de ail o he ma e ial s uc u e o he objec (ac ylic pain on he can as) aimed o de e mine he possible in luence o he ma e ial s uc u e on he gene a ion o unwan ed poin s du ing he c ea ion o a 3D ealis ic model using he pho og amme y me hod. Fu he mo e, he LiDAR senso assumed a sligh de o ma ion in he s uc u e o he 3D model. A 3D lase scanning mic oscope Keyence VK-X3000 (Keyence In e na ional NV/SA, Mechelen, Belgium) was used o inspec he ma e ial s uc u e isually [33]. This de ice ea u es scanning ha p o ides adap abili y o iden i y he mino su ace ea u es o a ma e ial. The objec was scanned using scanning de ices in dayligh in he na u al en i onmen o he in e io o an a galle y, aiming o cap u e he objec ’s colo in such a way ha i is pe cei ed by human ision in his en i onmen . The ligh ing condi ions me he s anda d D65 (S anda d Illuminan ) in he in e io . Fu he mo e, he a objec was pho og aphed in he da k o e alua e whe he i was signi ican ly affec ed by changes in ligh ing condi ions in he a galle y a dusk. 2.2. Digi al Image Cap u e Digi al de ices ha e g adually eplaced analog sensing de ices. Digi al compac came as and Digi al Single-Lens Re lex (DSLR) came as ha e g adually been supplemen ed by 360° came as and scanne s, RGBd came as, Ligh De ec ion And Ranging (LiDAR) senso echnology, and o he ypes o digi al de ices. Cu en ly, SMART mobile de ices, such as mobile phones and able s, a e al eady commonly used o hese pu poses, and emphasis is placed on low-cos me hods and p ocedu es [15,17,19,20]. An inno a i e mobile de ice wi h LiDAR echnology was used in his expe imen . An iPad 11″ P o sma able om Apple was used o cap u e and digi ize he a wo k. This sma de ice has a high-quali y came a wi h high esolu ion and a LiDAR senso [34]. This sma de ice was used o he 3D Figu e 1. Digi iza ion o an a objec : (a) 2D digi ized objec and de ail ma ked in ed; (b) ma ix o pa ial de ails he yellow ange o he image; and (c) isualiza ion o he de ail o he s uc u e and colo o a pa ial pa o he objec . Figu e 1a shows a pho og aph o a digi ized objec wi h a ma ked space in which he alue o he di ec g een colo RGB = 117, 134, 123 was measu ed by he Colo ca ch NANO colo ime e . Figu e 1b shows a de ailed ma ix o he colo s uc u e. The link ma ix is ma ked in he yellow ange o 5.563 µ m × 4.171 µ m. Figu e 1b shows he de ails o he ma e ial s uc u e o he objec o he ma ked elemen o he ma ix shown in Figu e 1c. This essen ial isual inspec ion o he colo o he objec in de ail o he ma e ial s uc u e o he objec (ac ylic pain on he can as) aimed o de e mine he possible in luence o he ma e ial s uc u e on he gene a ion o unwan ed poin s du ing he c ea ion o a 3D ealis ic model using he pho og amme y me hod. Fu he mo e, he LiDAR senso assumed a sligh de o ma ion in he s uc u e o he 3D model. A 3D lase scanning mic oscope Keyence VK-X3000 (Keyence In e na ional NV/SA, Mechelen, Belgium) was used o inspec he ma e ial s uc u e isually [ 33 ]. This de ice ea u es scanning ha p o ides adap abili y o iden i y he mino su ace ea u es o a ma e ial. The objec was scanned using scanning de ices in dayligh in he na u al en i onmen o he in e io o an a galle y, aiming o cap u e he objec ’s colo in such a way ha i is pe cei ed by human ision in his en i onmen . The ligh ing condi ions me he s anda d D65 (S anda d Illuminan ) in he in e io . Fu he mo e, he a objec was pho og aphed in he da k o e alua e whe he i was signi ican ly a ec ed by changes in ligh ing condi ions in he a galle y a dusk. 2.2. Digi al Image Cap u e Digi al de ices ha e g adually eplaced analog sensing de ices. Digi al compac came as and Digi al Single-Lens Re lex (DSLR) came as ha e g adually been supplemen ed by 360 ◦ came as and scanne s, RGBd came as, Ligh De ec ion And Ranging (LiDAR) senso echnology, and o he ypes o digi al de ices. Cu en ly, SMART mobile de ices, such as mobile phones and able s, a e al eady commonly used o hese pu poses, and emphasis is placed on low-cos me hods and p ocedu es [ 15 , 17 , 19 , 20 ]. An inno a i e mobile de ice wi h LiDAR echnology was used in his expe imen . An iPad 11 ′′ P o sma able om Apple was used o cap u e and digi ize he a wo k. This sma de ice has a high-quali y came a wi h high esolu ion and a LiDAR senso [ 34 ]. This sma de ice was used o he 3D econs uc ion o a wo k o a using g ound image pho og amme y. A he same ime, he ee applica ion Scani e se om Nian ic Labs was used o compa e he Elec onics 2024,13, 4431 4 o 15 quali y o he 3D model, which was in ended di ec ly o he 3D digi iza ion o objec s and spaces using he LiDAR senso [ 35 ]. Bo h me hods a e desc ibed in he ollowing sec ion. The p o essional 3D modeling SW Agiso Me ashape P o essional (online: agiso .cz, 2021) om he company Agiso (S . Pe e sbu g, Russia) was used o he 3D econs uc ion o he a wo k and analysis o he colo 3D ep oduc ion [36]. 2.3. 3D Recons uc ion by Pho og amme y Me hod The S M (S uc u e om Mo ion) pho og amme y me hod calcula es he loca ion o an objec in 3D space based on he desc ip ion o in o ma ion ob ained om indi idual images aken om mul iple angles. In he case o a speci ic objec , he 3D econs uc ion desc ibed below in ol es 250 pho os, whe e he algo i hm based on he p inciple o iangula ion inds s anda d bodies in indi idual pho os and calcula es he indi idual posi ion o he came a a ound he objec . By subsequen ly calcula ing he Dense Cloud (cloud o poin s), each poin ob ains i s own x, y, and z coo dina es and hus de ines basic in o ma ion abou he posi ion, size, and geome y o he objec loca ed in space. Figu e 2shows he p inciple o he pho og amme y me hod [14,37]. I’+l1X+l2Y+l3Z+l4 l9X+l10Y+l11Z+1=0∪J′+l5X+l6Y+l7Z+l8 l9X+l10Y+l11Z+1=0 (1) whe e I′=I−I0 ; J′=J−J0 ; l1−l11 is he Di ec Linea T ans o ma ion Pa ame e (DLTP). The coe icien s l1 o l11 a e unc ions o ex e io landma ks and in e io landma ks. The ini ial alues o he ex e nal and in e nal o ien a ion elemen s a e unnecessa y in he calcula ion. The DLT equa ion can be used in he pho og amme y o consume -class digi al came as [ 19 , 37 ]. The basic gene a ed poin cloud o he digi ized objec is shown in Figu e 3. In his case, he basic 3D poin cloud is c ea ed om 24 images, om which 13,828 poin s we e gene a ed. Elec onics 2024, 13, x FOR PEER REVIEW 4 o 16 econs uc ion o a wo k o a using g ound image pho og amme y. A he same ime, he ee applica ion Scani e se om Nian ic Labs was used o compa e he quali y o he 3D model, which was in ended di ec ly o he 3D digi iza ion o objec s and spaces using he LiDAR senso [35]. Bo h me hods a e desc ibed in he ollowing sec ion. The p o essional 3D modeling SW Agiso Me ashape P o essional (online: agiso .cz, 2021) om he company Agiso (S . Pe e sbu g, Russia) was used o he 3D econs uc ion o he a wo k and analysis o he colo 3D ep oduc ion [36]. 2.3. 3D Recons uc ion by Pho og amme y Me hod The S M (S uc u e om Mo ion) pho og amme y me hod calcula es he loca ion o an objec in 3D space based on he desc ip ion o in o ma ion ob ained om indi idual images aken om mul iple angles. In he case o a speci ic objec , he 3D econs uc ion desc ibed below in ol es 250 pho os, whe e he algo i hm based on he p inciple o iangula ion inds s anda d bodies in indi idual pho os and calcula es he indi idual posi ion o he came a a ound he objec . By subsequen ly calcula ing he Dense Cloud (cloud o poin s), each poin ob ains i s own x, y, and z coo dina es and hus de ines basic in o ma ion abou he posi ion, size, and geome y o he objec loca ed in space. Figu e 2 shows he p inciple o he pho og amme y me hod [14,37]. Figu e 2. The basic p inciple o he S uc u e om Mo ion (S M) me hod [37]. I’  lXl Yl Zl  lXl Yl Z10 ∪ J´ lXl Yl Zl  lXl Yl Z10 (1) whe e I´  I  I; J´JJ ; ll  is he Di ec Linea T ans o ma ion Pa ame e (DLTP). The coefficien s l o l a e unc ions o ex e io landma ks and in e io landma ks. The ini ial alues o he ex e nal and in e nal o ien a ion elemen s a e unnecessa y in he calcula ion. The DLT equa ion can be used in he pho og amme y o consume -class digi al came as [19,37]. The basic gene a ed poin cloud o he digi ized objec is shown in Figu e 3. In his case, he basic 3D poin cloud is c ea ed om 24 images, om which 13,828 poin s we e gene a ed. (a) (b) (c) (d) Figu e 2. The basic p inciple o he S uc u e om Mo ion (S M) me hod [37]. Elec onics 2024, 13, x FOR PEER REVIEW 4 o 16 econs uc ion o a wo k o a using g ound image pho og amme y. A he same ime, he ee applica ion Scani e se om Nian ic Labs was used o compa e he quali y o he 3D model, which was in ended di ec ly o he 3D digi iza ion o objec s and spaces using he LiDAR senso [35]. Bo h me hods a e desc ibed in he ollowing sec ion. The p o essional 3D modeling SW Agiso Me ashape P o essional (online: agiso .cz, 2021) om he company Agiso (S . Pe e sbu g, Russia) was used o he 3D econs uc ion o he a wo k and analysis o he colo 3D ep oduc ion [36]. 2.3. 3D Recons uc ion by Pho og amme y Me hod The S M (S uc u e om Mo ion) pho og amme y me hod calcula es he loca ion o an objec in 3D space based on he desc ip ion o in o ma ion ob ained om indi idual images aken om mul iple angles. In he case o a speci ic objec , he 3D econs uc ion desc ibed below in ol es 250 pho os, whe e he algo i hm based on he p inciple o iangula ion inds s anda d bodies in indi idual pho os and calcula es he indi idual posi ion o he came a a ound he objec . By subsequen ly calcula ing he Dense Cloud (cloud o poin s), each poin ob ains i s own x, y, and z coo dina es and hus de ines basic in o ma ion abou he posi ion, size, and geome y o he objec loca ed in space. Figu e 2 shows he p inciple o he pho og amme y me hod [14,37]. Figu e 2. The basic p inciple o he S uc u e om Mo ion (S M) me hod [37]. I’  lXl Yl Zl  lXl Yl Z10 ∪ J´ lXl Yl Zl  lXl Yl Z10 (1) whe e I´  I  I; J´JJ ; ll  is he Di ec Linea T ans o ma ion Pa ame e (DLTP). The coefficien s l o l a e unc ions o ex e io landma ks and in e io landma ks. The ini ial alues o he ex e nal and in e nal o ien a ion elemen s a e unnecessa y in he calcula ion. The DLT equa ion can be used in he pho og amme y o consume -class digi al came as [19,37]. The basic gene a ed poin cloud o he digi ized objec is shown in Figu e 3. In his case, he basic 3D poin cloud is c ea ed om 24 images, om which 13,828 poin s we e gene a ed. (a) (b) (c) (d) Figu e 3. C ea ion o a 3D model using he S M pho og amme y me hod: (a) Digi ized objec ; (b) posi ion o 24 pho os om which he basic cloud o poin s is c ea ed; (c) Dense Cloud gene a ion; (d) he esul ing 3D ex u e model o he a wo k. Elec onics 2024,13, 4431 5 o 15 Figu e 3a shows a 2D model o he a wo k, and Figu e 3b shows a 3D poin cloud gene a ed om 24 pho og aphs ha we e used o apply he S M me hod. The p inciple o his me hod is shown in Figu e 2. The gene a ed poin s p o ide in o ma ion abou he econs uc ed objec ’s posi ion, geome y, and colo . F om his basic in o ma ion in he p ima y cloud o poin s, he poin s ha o m he Dense Cloud a e added by u he calcula ion. This high numbe o poin s will mo e speci ically display he shape o he 3D objec and i s posi ion in space, as shown in Figu e 3c. This will c ea e a comple e poin model, om which i can be de e mined in which places he calcula ion did no de ine poin s and whe e i is necessa y o add poin s. The esul ing 3D model co esponds o he shape and s uc u e o he physical objec in eal space, as shown in Figu e 3d. Le us add ha he gene a ed Dense Cloud 3D model con ains 413,688 indi idual poin s. SW Agiso Me ashape P o essional was used o 3D modeling. In he ollowing sec ion, he me hod o 3D econs uc ion using a sma de ice wi h a LiDAR senso is desc ibed and isualized in mo e de ail. 2.4. 3D Recons uc ion by a LiDAR Senso In he case o using a LiDAR senso and an image p ocessing applica ion, pho og aphs a e no used o he p ima y econs uc ion o he poin cloud. The inished 3D digi al model o he a wo k was scanned by he senso di ec ly in he ee Scani e se applica ion and hen con e ed o 3D g aphic 3D SW Agiso Me ashape P o essional. A poin cloud was gene a ed om his 3D model o subsequen image analysis. This me hod was chosen o compa e he quali y o he digi al ep oduc ion o a wo k o a cap u ed by he same cap u e de ice, which is an iPad 11′′ P o able . Figu e 4shows he 3D econs uc ion o he objec using he LiDAR senso . The Scani e se applica ion was used o c ea e a 3D model. A 3D model o he objec is shown in Figu e 4b. This ex u ed 3D model was ans e ed o SW Agiso o gene a e a cloud o poin s. Figu e 4c shows he 3D model expo ed o he Agiso 3D SW. A Dense Cloud wi h a simila body 23 was gene a ed in his case. Figu e 4d shows he de ails o he gene a ed 3D poin s. Figu e 5shows he de ails o he Dense Cloud. Elec onics 2024, 13, x FOR PEER REVIEW 5 o 16 Figu e 3. C ea ion o a 3D model using he S M pho og amme y me hod: (a) Digi ized objec ; (b) posi ion o 24 pho os om which he basic cloud o poin s is c ea ed; (c) Dense Cloud gene a ion; (d) he esul ing 3D ex u e model o he a wo k. Figu e 3a shows a 2D model o he a wo k, and Figu e 3b shows a 3D poin cloud gene a ed om 24 pho og aphs ha we e used o apply he S M me hod. The p inciple o his me hod is shown in Figu e 2. The gene a ed poin s p o ide in o ma ion abou he econs uc ed objec ’s posi ion, geome y, and colo . F om his basic in o ma ion in he p ima y cloud o poin s, he poin s ha o m he Dense Cloud a e added by u he calcula ion. This high numbe o poin s will mo e speci ically display he shape o he 3D objec and i s posi ion in space, as shown in Figu e 3c. This will c ea e a comple e poin model, om which i can be de e mined in which places he calcula ion did no de ine poin s and whe e i is necessa y o add poin s. The esul ing 3D model co esponds o he shape and s uc u e o he physical objec in eal space, as shown in Figu e 3d. Le us add ha he gene a ed Dense Cloud 3D model con ains 413,688 indi idual poin s. SW Agiso Me ashape P o essional was used o 3D modeling. In he ollowing sec ion, he me hod o 3D econs uc ion using a sma de ice wi h a LiDAR senso is desc ibed and isualized in mo e de ail. 2.4. 3D Recons uc ion by a LiDAR Senso In he case o using a LiDAR senso and an image p ocessing applica ion, pho og aphs a e no used o he p ima y econs uc ion o he poin cloud. The inished 3D digi al model o he a wo k was scanned by he senso di ec ly in he ee Scani e se applica ion and hen con e ed o 3D g aphic 3D SW Agiso Me ashape P o essional. A poin cloud was gene a ed om his 3D model o subsequen image analysis. This me hod was chosen o compa e he quali y o he digi al ep oduc ion o a wo k o a cap u ed by he same cap u e de ice, which is an iPad 11″ P o able . Figu e 4 shows he 3D econs uc ion o he objec using he LiDAR senso . The Scani e se applica ion was used o c ea e a 3D model. A 3D model o he objec is shown in Figu e 4b. This ex u ed 3D model was ans e ed o SW Agiso o gene a e a cloud o poin s. Figu e 4c shows he 3D model expo ed o he Agiso 3D SW. A Dense Cloud wi h a simila body 23 was gene a ed in his case. Figu e 4d shows he de ails o he gene a ed 3D poin s. Figu e 5 shows he de ails o he Dense Cloud. (a) (b) (c) (d) Figu e 4. C ea ing a 3D model using a LiDAR senso : (a) digi ized objec ; (b) 3D model gene a ed by Scani e se; (c) 3D ex u e model impo ed in o Agiso 3D SW; and (d) gene a ed poin cloud om he ex u ed 3D model. (a) (b) Figu e 4. C ea ing a 3D model using a LiDAR senso : (a) digi ized objec ; (b) 3D model gene a ed by Scani e se; (c) 3D ex u e model impo ed in o Agiso 3D SW; and (d) gene a ed poin cloud om he ex u ed 3D model. Elec onics 2024, 13, x FOR PEER REVIEW 5 o 16 Figu e 3. C ea ion o a 3D model using he S M pho og amme y me hod: (a) Digi ized objec ; (b) posi ion o 24 pho os om which he basic cloud o poin s is c ea ed; (c) Dense Cloud gene a ion; (d) he esul ing 3D ex u e model o he a wo k. Figu e 3a shows a 2D model o he a wo k, and Figu e 3b shows a 3D poin cloud gene a ed om 24 pho og aphs ha we e used o apply he S M me hod. The p inciple o his me hod is shown in Figu e 2. The gene a ed poin s p o ide in o ma ion abou he econs uc ed objec ’s posi ion, geome y, and colo . F om his basic in o ma ion in he p ima y cloud o poin s, he poin s ha o m he Dense Cloud a e added by u he calcula ion. This high numbe o poin s will mo e speci ically display he shape o he 3D objec and i s posi ion in space, as shown in Figu e 3c. This will c ea e a comple e poin model, om which i can be de e mined in which places he calcula ion did no de ine poin s and whe e i is necessa y o add poin s. The esul ing 3D model co esponds o he shape and s uc u e o he physical objec in eal space, as shown in Figu e 3d. Le us add ha he gene a ed Dense Cloud 3D model con ains 413,688 indi idual poin s. SW Agiso Me ashape P o essional was used o 3D modeling. In he ollowing sec ion, he me hod o 3D econs uc ion using a sma de ice wi h a LiDAR senso is desc ibed and isualized in mo e de ail. 2.4. 3D Recons uc ion by a LiDAR Senso In he case o using a LiDAR senso and an image p ocessing applica ion, pho og aphs a e no used o he p ima y econs uc ion o he poin cloud. The inished 3D digi al model o he a wo k was scanned by he senso di ec ly in he ee Scani e se applica ion and hen con e ed o 3D g aphic 3D SW Agiso Me ashape P o essional. A poin cloud was gene a ed om his 3D model o subsequen image analysis. This me hod was chosen o compa e he quali y o he digi al ep oduc ion o a wo k o a cap u ed by he same cap u e de ice, which is an iPad 11″ P o able . Figu e 4 shows he 3D econs uc ion o he objec using he LiDAR senso . The Scani e se applica ion was used o c ea e a 3D model. A 3D model o he objec is shown in Figu e 4b. This ex u ed 3D model was ans e ed o SW Agiso o gene a e a cloud o poin s. Figu e 4c shows he 3D model expo ed o he Agiso 3D SW. A Dense Cloud wi h a simila body 23 was gene a ed in his case. Figu e 4d shows he de ails o he gene a ed 3D poin s. Figu e 5 shows he de ails o he Dense Cloud. (a) (b) (c) (d) Figu e 4. C ea ing a 3D model using a LiDAR senso : (a) digi ized objec ; (b) 3D model gene a ed by Scani e se; (c) 3D ex u e model impo ed in o Agiso 3D SW; and (d) gene a ed poin cloud om he ex u ed 3D model. (a) (b) Figu e 5. Gene a ed Dense Cloud: (a) 3D S M pho og amme y me hod and (b) LiDAR senso . Elec onics 2024,13, 4431 6 o 15 Figu e 5shows he de ails o he digi ized a wo k’s Dense Cloud s uc u e. Figu e 5a shows he de ails o he Dense Cloud and indi idual poin s gene a ed om he pho og aphs and he p ima y poin cloud p oduced by he S M pho og amme y me hod. In o al, 413,688 indi idual poin s we e gene a ed in he 3D model. Figu e 5b shows he de ails o he 23 poin s gene a ed om he 3D ex u e model made by he senso LiDAR. Indi idual poin s p o ide colo in o ma ion ha ul ima ely de ines indi idual poin s wi h a colo alue o #758605. 3. Colo ime y and Colo Analysis Colo ime y is he science o colo and ligh . This ield deals wi h he colo in e - p e a ion o objec s and he en i onmen , human ision, and colo ep oduc ion. Ligh is elec omagne ic adia ion. The spec um includes isible ligh (400–700 nm), which is isible o he human eye. The isible colo o ligh is based on he wa eleng h λ and subjec i e pe cep ion by he human eye. Colo image p ocessing is also ela ed o his. Fo hese pu poses, colo models and gamu s de ine he possibili ies o he colo display o indi idual ones and hei maximum ange. To uni y colo ep oduc ion, he in e na ional s anda d CIE 1931 was adop ed in 1931, which is based on cu en mode n echnology and p ocedu es ac oss manu ac u ing and scien i ic ields. 3.1. Colo Model and Gamu In his expe imen , in which a eal objec is ans o med in o a digi al o m, he RGB ( ed, g een, blue) colo model and he sRGB colo space (gamu ) a e used. Figu e 6shows hei g aphical ep esen a ion. Elec onics 2024, 13, x FOR PEER REVIEW 6 o 16 Figu e 5. Gene a ed Dense Cloud: (a) 3D S M pho og amme y me hod and (b) LiDAR senso . Figu e 5 shows he de ails o he digi ized a wo k’s Dense Cloud s uc u e. Figu e 5a shows he de ails o he Dense Cloud and indi idual poin s gene a ed om he pho og aphs and he p ima y poin cloud p oduced by he S M pho og amme y me hod. In o al, 413,688 indi idual poin s we e gene a ed in he 3D model. Figu e 5b shows he de ails o he 23 poin s gene a ed om he 3D ex u e model made by he senso LiDAR. Indi idual poin s p o ide colo in o ma ion ha ul ima ely de ines indi idual poin s wi h a colo alue o #758605. 3. Colo ime y and Colo Analysis Colo ime y is he science o colo and ligh . This ield deals wi h he colo in e p e a ion o objec s and he en i onmen , human ision, and colo ep oduc ion. Ligh is elec omagne ic adia ion. The spec um includes isible ligh (400–700 nm), which is isible o he human eye. The isible colo o ligh is based on he wa eleng h λ and subjec i e pe cep ion by he human eye. Colo image p ocessing is also ela ed o his. Fo hese pu poses, colo models and gamu s de ine he possibili ies o he colo display o indi idual ones and hei maximum ange. To uni y colo ep oduc ion, he in e na ional s anda d CIE 1931 was adop ed in 1931, which is based on cu en mode n echnology and p ocedu es ac oss manu ac u ing and scien i ic ields. 3.1. Colo Model and Gamu In his expe imen , in which a eal objec is ans o med in o a digi al o m, he RGB ( ed, g een, blue) colo model and he sRGB colo space (gamu ) a e used. Figu e 6 shows hei g aphical ep esen a ion. (a) (b) Figu e 6. Colo ime y: (a) RGB colo model and (b) sRGB colo space (gamu ). Figu e 6a shows he RGB (Red, G een, Blue) colo model. This is he basic colo model o RGB p ima y colo s. The RGB colo model ope a es using ligh componen s. This model p ima ily a ge s digi al imaging and imaging de ices (DSLR displays). The seconda y colo s c ea ed by mixing he p ima y RGB colo s a e CMY (Cyan, Magen a, Yellow). Mixing all h ee essen ial componen s o RGB c ea es whi e ligh (W). Figu e 6b shows he CIE1931 ich oma ic iangle, s anda dizing wo k wi h colo s since 1931. The sRGB gamu (colo space) is ep esen ed in he indica ed ich oma ic iangle in Figu e 6. This space ep esen s he maximum colo ange in which a digi al sensing o imaging de ice can ope a e. I cu en ly displays he colo spec um and ange o mos digi al imaging and sensing de ices in he sRGB gamu . The e o e, he RGB model was Figu e 6. Colo ime y: (a) RGB colo model and (b) sRGB colo space (gamu ). Figu e 6a shows he RGB (Red, G een, Blue) colo model. This is he basic colo model o RGB p ima y colo s. The RGB colo model ope a es using ligh componen s. This model p ima ily a ge s digi al imaging and imaging de ices (DSLR displays). The seconda y colo s c ea ed by mixing he p ima y RGB colo s a e CMY (Cyan, Magen a, Yellow). Mixing all h ee essen ial componen s o RGB c ea es whi e ligh (W). Figu e 6b shows he CIE1931 ich oma ic iangle, s anda dizing wo k wi h colo s since 1931. The sRGB gamu (colo space) is ep esen ed in he indica ed ich oma ic iangle in Figu e 6. This space ep esen s he maximum colo ange in which a digi al sensing o imaging de ice can ope a e. I cu en ly displays he colo spec um and ange o mos digi al imaging and sensing de ices in he sRGB gamu . The e o e, he RGB model was chosen as he de aul colo model o his expe imen . The alues o he indi idual colo componen s o ligh o di ec colo #758605 (Hex Colo Value) a e as ollows: R = 117; G = 134; B = 5. In connec ion wi h Sec ion 3.1. subsequen ly, in Sec ion 3.3, a en ion is Elec onics 2024,13, 4431 7 o 15 paid o he colo space and gamu L*a*b*, which is sui able o subsequen wo k wi h colo ep oduc ion and display in he VR en i onmen . 3.2. Colo Value #758605 Segmen a ion In Agiso 3D modeling, he SW en i onmen can wo k wi h in o ma ion in he colo o indi idual poin s o g oups o poin s in he Dense Cloud in RGB and HSV colo models. This can be pe o med wi h a p ecisely de ined Hex Colo Value, as shown in Figu e 7. Elec onics 2024, 13, x FOR PEER REVIEW 7 o 16 chosen as he de aul colo model o his expe imen . The alues o he indi idual colo componen s o ligh o di ec colo #758605 (Hex Colo Value) a e as ollows: R = 117; G = 134; B = 5. In connec ion wi h Sec ion 3.1. subsequen ly, in Sec ion 3.3, a en ion is paid o he colo space and gamu L*a*b*, which is sui able o subsequen wo k wi h colo ep oduc ion and display in he VR en i onmen . 3.2. Colo Value #758605 Segmen a ion In Agiso 3D modeling, he SW en i onmen can wo k wi h in o ma ion in he colo o indi idual poin s o g oups o poin s in he Dense Cloud in RGB and HSV colo models. This can be pe o med wi h a p ecisely de ined Hex Colo Value, as shown in Figu e 7. (a) (b) (c) Figu e 7. S M—Poin s Segmen a ion #758605: (a) Dense Cloud 3D model using S M pho og amme y; (b) segmen a ion poin s by colo G#758605; (c) body #758605 in Dense Cloud. Figu e 7 shows he segmen a ion o poin s ca ying in o ma ion abou colo alue #758605. Figu e 7a shows a 3D Dense Cloud model o indi idual poin s gene a ed om pho og aphs and c ea ed by he S M pho og amme ic me hod. Figu e 7b isualizes he SW Agiso en i onmen o wo king wi h Dense Cloud and in o ma ion abou he colo alue ca ied by each poin . The 3D SW wo ks wi h RGB and HSV (Hue, Sa u a ion, Value) colo models in his case. Values can be di ec ly nume ically de ined in Hex o ma . Howe e , i should be no ed ha he RGB colo model does no con ain onal o o he alues such as Hue, Sa u a ion, and Value in he HSV colo model. Figu e 7c shows an example o he inal segmen a ion o poin s wi h a colo alue o #758605 in he o al numbe o clouding poin s in he 3D model. Figu e 8 shows he p ocess o segmen ing he poin s using colo in o ma ion #758605. Figu e 8a shows he 3D ex u e model cap u ed by he LiDAR senso in Agiso 3D SW. A poin cloud con aining 17 poin s was gene a ed om his ex u e model, as shown in Figu e 8b. The segmen a ion p ocess o he poin wi h colo in o ma ion #758605 is iden ical o ha o he S M me hod, as shown in Figu e 7b. As shown in Figu e 8c, no single poin wi h a colo alue o #758605 was de ined ou o he o al numbe o poin s. Fo his eason, he segmen a ion o poin s in he 3D models by he LiDAR senso is no shown in Sec ion 4.1. (a) (b) (c) Figu e 8. LiDAR—Segmen a ion o poin s #758605: (a) 3D model using LiDAR senso ; (b) Segmen a ion o poin s by colo G#758605; (c) de ail o he poin s gene a ed in Dense Cloud. Figu e 7. S M—Poin s Segmen a ion #758605: (a) Dense Cloud 3D model using S M pho og amme y; (b) segmen a ion poin s by colo G#758605; (c) body #758605 in Dense Cloud. Figu e 7shows he segmen a ion o poin s ca ying in o ma ion abou colo alue #758605. Figu e 7a shows a 3D Dense Cloud model o indi idual poin s gene a ed om pho og aphs and c ea ed by he S M pho og amme ic me hod. Figu e 7b isualizes he SW Agiso en i onmen o wo king wi h Dense Cloud and in o ma ion abou he colo alue ca ied by each poin . The 3D SW wo ks wi h RGB and HSV (Hue, Sa u a ion, Value) colo models in his case. Values can be di ec ly nume ically de ined in Hex o ma . Howe e , i should be no ed ha he RGB colo model does no con ain onal o o he alues such as Hue, Sa u a ion, and Value in he HSV colo model. Figu e 7c shows an example o he inal segmen a ion o poin s wi h a colo alue o #758605 in he o al numbe o clouding poin s in he 3D model. Figu e 8shows he p ocess o segmen ing he poin s using colo in o ma ion #758605. Figu e 8a shows he 3D ex u e model cap u ed by he LiDAR senso in Agiso 3D SW. A poin cloud con aining 17 poin s was gene a ed om his ex u e model, as shown in Figu e 8b. The segmen a ion p ocess o he poin wi h colo in o ma ion #758605 is iden ical o ha o he S M me hod, as shown in Figu e 7b. As shown in Figu e 8c, no single poin wi h a colo alue o #758605 was de ined ou o he o al numbe o poin s. Fo his eason, he segmen a ion o poin s in he 3D models by he LiDAR senso is no shown in Sec ion 4.1. Elec onics 2024, 13, x FOR PEER REVIEW 7 o 16 chosen as he de aul colo model o his expe imen . The alues o he indi idual colo componen s o ligh o di ec colo #758605 (Hex Colo Value) a e as ollows: R = 117; G = 134; B = 5. In connec ion wi h Sec ion 3.1. subsequen ly, in Sec ion 3.3, a en ion is paid o he colo space and gamu L*a*b*, which is sui able o subsequen wo k wi h colo ep oduc ion and display in he VR en i onmen . 3.2. Colo Value #758605 Segmen a ion In Agiso 3D modeling, he SW en i onmen can wo k wi h in o ma ion in he colo o indi idual poin s o g oups o poin s in he Dense Cloud in RGB and HSV colo models. This can be pe o med wi h a p ecisely de ined Hex Colo Value, as shown in Figu e 7. (a) (b) (c) Figu e 7. S M—Poin s Segmen a ion #758605: (a) Dense Cloud 3D model using S M pho og amme y; (b) segmen a ion poin s by colo G#758605; (c) body #758605 in Dense Cloud. Figu e 7 shows he segmen a ion o poin s ca ying in o ma ion abou colo alue #758605. Figu e 7a shows a 3D Dense Cloud model o indi idual poin s gene a ed om pho og aphs and c ea ed by he S M pho og amme ic me hod. Figu e 7b isualizes he SW Agiso en i onmen o wo king wi h Dense Cloud and in o ma ion abou he colo alue ca ied by each poin . The 3D SW wo ks wi h RGB and HSV (Hue, Sa u a ion, Value) colo models in his case. Values can be di ec ly nume ically de ined in Hex o ma . Howe e , i should be no ed ha he RGB colo model does no con ain onal o o he alues such as Hue, Sa u a ion, and Value in he HSV colo model. Figu e 7c shows an example o he inal segmen a ion o poin s wi h a colo alue o #758605 in he o al numbe o clouding poin s in he 3D model. Figu e 8 shows he p ocess o segmen ing he poin s using colo in o ma ion #758605. Figu e 8a shows he 3D ex u e model cap u ed by he LiDAR senso in Agiso 3D SW. A poin cloud con aining 17 poin s was gene a ed om his ex u e model, as shown in Figu e 8b. The segmen a ion p ocess o he poin wi h colo in o ma ion #758605 is iden ical o ha o he S M me hod, as shown in Figu e 7b. As shown in Figu e 8c, no single poin wi h a colo alue o #758605 was de ined ou o he o al numbe o poin s. Fo his eason, he segmen a ion o poin s in he 3D models by he LiDAR senso is no shown in Sec ion 4.1. (a) (b) (c) Figu e 8. LiDAR—Segmen a ion o poin s #758605: (a) 3D model using LiDAR senso ; (b) Segmen a ion o poin s by colo G#758605; (c) de ail o he poin s gene a ed in Dense Cloud. Figu e 8. LiDAR—Segmen a ion o poin s #758605: (a) 3D model using LiDAR senso ; (b) Segmen a- ion o poin s by colo G#758605; (c) de ail o he poin s gene a ed in Dense Cloud. 3.3. Colo Model and Gamu in Vi ual Reali y The p e ious sec ions p esen wo k wi h he RGB (Red, G een, Blue) colo model and he sRGB colo space, in which he mos common sensing and display de ices wo k. Howe e , he ype o VR headse and echnical pa ame e s mus be conside ed when Elec onics 2024,13, 4431 8 o 15 iewing a VR en i onmen . In pa icula , he ype and esolu ion o he VR headse undamen ally demons a e he quali y o displaying ealis ic scenes o models. I should be no ed ha he ypes o display de ices o VR and hei echnical pa ame e s di e signi ican ly, and he colo space o gamu needs o be p ecisely de ined. These a ibu es ha e no ye been s anda dized o VR echnology. When c ea ing ealis ic 3D models and scenes, i is necessa y o know in ad ance he ype and pa ame e s o he display VR headse and he inal ou pu p esen a ion. Image p ocessing needs o be adap ed o hese ac o s, which may be di e en . An Oculus Ques 2 VR headse was used in his s udy [ 38 ]. Almos all s anda d 2D and display de ices ope a e in he sRGB colo space [ 39 , 40 ]. Howe e , in he case o VR imaging echniques, s anda d colo models and gamu s a e no used. Conside ing he di e ence be ween classic and VR head-moun ed displays (HDM) is necessa y. Howe e , di e en HDMs use di e en colo spaces and speci ica ions. The e o e, he colo s will be di e en om hose o he s anda d display. Howe e , di e en HDMs isually in e p e he colo s di e en ly. The e o e, i is necessa y o conside he ype o VR headse du ing he i ual p esen a ion a he beginning o he p ocess. Olde Oculus and Ques /Ri headse s in e p e ed he image in he Rec.709 colo space. This is he mos common colo space cha ac e is ic o In e ne con en . Howe e , his colo space does no ha e HDR (High Dynamic Range) echnology, which simul aneously enables he ex ended ep oduc ion o de ails in da k and ligh pa ially cap u ed scenes. The e o e, he Oculus Ques 2 VR Headse was used in his s udy o isualize he a wo k in a VR en i onmen . This VR display de ice ope a es in he Rec.2020(HDR) de aul gamu and includes a s anda dized D65 whi e poin co esponding o dayligh . This is a di e en ange o colo simplici y o display de ices. In connec ion wi h he di e en colo spaces o he sensing and display de ices in his expe imen , i is app op ia e o use he L*a*b* gamu (CIE 1976). The L*a*b* colo space is de i ed om he i s s anda dized CIE XYZ colo space (CIE 1931) and is independen o a pa icula sensing o display de ice. This colo space con ains he ull colo ange o he ich oma ic iangle, as shown in Figu es 6and 9. Also ela ed o he CIE XYZ colo space is he basic s anda dized colo scale o Colo Checke Classic (X-Ri e), used in colo sensing and o colo calib a ion o display de ices acco ding o in e na ional s anda ds [ 41 ]. I is e iden om he abo e ha i is necessa y o ans o m indi idual colo models and gamu s among hemsel es [42]. As shown in Figu e 9, i is necessa y o conside he choice o colo models and colo spaces depending on he desi ed inal ou pu o ealis ic colo ep oduc ion. Figu e 9a shows he s anda d colo Colo Checke (X-Ri e) used in he image cap u e and calib a ion de ice. The indi idual colo posi ions in he ch oma ic diag am a e shown in Figu e 9b. Figu e 9c shows he colo model L*a*b*. The indi idual colo anges o he Rec.2020(HDR), sRGB, and L*a*b* gamu s a e shown in he CIE 1931 ch oma ic diag am in Figu e 9d. F om he abo e, i ollows ha ealis ic 3D econs uc ion o wo ks o a and hei isualiza ion in a VR en i onmen mus pay a en ion o he issue o colo s, colo models, and games in each o he indi idual s eps o he en i e p ocedu e. Elec onics 2024,13, 4431 9 o 15 Elec onics 2024, 13, x FOR PEER REVIEW 9 o 16 (a) (b) (c) (d) Figu e 9. CIE XYZ 1931 s anda dized colo space: (a) Basic Colo Checke s anda dized colo gamu ; (b) posi ion o indi idual s anda dized colo s in he CIE 1931 ch oma ic diag am; (c) colo model L*a*b*; (d) CIE 1931 ch oma ici y diag am wi h Rec.2020 gamu s; sRGB and L*a*b. 4. Resul s As men ioned in he p e ious sec ions, he a wo k was scanned using an iPad 11″ P o sma de ice wi h a LiDAR senso om Apple. The a wo k was sho indoo s in na u al dayligh and hen in na u al wiligh using a able came a. Fo he 3D econs uc ion o he objec , wo diffe en me hods we e chosen using one scanning de ice. The 3D econs uc ion o he objec using he S M pho og amme ic me hod uses he in eg a ed came a o he sensing de ice. The second me hod o 3D econs uc ion uses a LiDAR senso and he di ec scanning o he Scani e se mobile applica ion o c ea e 3D models. The goal o using hese me hods is o compa e he quali y o he 3D econs uc ion o an objec using one scanning de ice o he image, as well as o he a ibu es, such as he scanning speed o he p ocess o p ocessing he image in o a 3D model. Table 1 shows he a ibu es o he e e ence pho os ha we e pa o he indi idual se ies om which he 3D models o he objec we e modeled using he S M me hod. In he case o 3D econs uc ion using LiDAR senso scanning, indi idual pho os we e no he sou ce o digi iza ion. The e o e, he image a ibu es a e no lis ed. Table 1 lis s he a ibu es and cha ac e is ics o he pho o se ies ha we e applied o c ea e a ealis ic 3D model. While he esolu ion, bi dep h, ape u e, and ocal leng h a e iden ical, he exposu e da a size and ISO a e diffe en o he e e ence pho os. These a ibu es affec he p og ess o 3D model c ea ion and he numbe o gene a ed poin s in he poin cloud ca ying colo in o ma ion. Table 2 shows he numbe o gene a ed poin s. Table 2 also Figu e 9. CIE XYZ 1931 s anda dized colo space: (a) Basic Colo Checke s anda dized colo gamu ; (b) posi ion o indi idual s anda dized colo s in he CIE 1931 ch oma ic diag am; (c) colo model L*a*b*; (d) CIE 1931 ch oma ici y diag am wi h Rec.2020 gamu s; sRGB and L*a*b. 4. Resul s As men ioned in he p e ious sec ions, he a wo k was scanned using an iPad 11 ′′ P o sma de ice wi h a LiDAR senso om Apple. The a wo k was sho indoo s in na u al dayligh and hen in na u al wiligh using a able came a. Fo he 3D econs uc ion o he objec , wo di e en me hods we e chosen using one scanning de ice. The 3D econs uc ion o he objec using he S M pho og amme ic me hod uses he in eg a ed came a o he sensing de ice. The second me hod o 3D econs uc ion uses a LiDAR senso and he di ec scanning o he Scani e se mobile applica ion o c ea e 3D models. The goal o using hese me hods is o compa e he quali y o he 3D econs uc ion o an objec using one scanning de ice o he image, as well as o he a ibu es, such as he scanning speed o he p ocess o p ocessing he image in o a 3D model. Table 1shows he a ibu es o he e e ence pho os ha we e pa o he indi idual se ies om which he 3D models o he objec we e modeled using he S M me hod. In he case o 3D econs uc ion using LiDAR senso scanning, indi idual pho os we e no he sou ce o digi iza ion. The e o e, he image a ibu es a e no lis ed. Table 1lis s he a ibu es and cha ac e is ics o he pho o se ies ha we e applied o c ea e a ealis ic 3D model. While he esolu ion, bi dep h, ape u e, and ocal leng h a e iden ical, he exposu e da a size and ISO a e di e en o he e e ence pho os. These a ibu es a ec he p og ess o 3D model c ea ion and he numbe o gene a ed poin s in he poin cloud ca ying colo in o ma ion. Table 2shows he numbe o gene a ed poin s. Table 2also shows he