3D MODELING OF STRUCTURES USING BREAK-LINES AND CORNERS IN 3D POINT CLOWD DATA

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1 3D MODELING OF STRUCTURES USING BREAK-LINES AND CORNERS IN 3D POINT CLOWD DATA Hiroshi YOKOYAMA a, Hirofumi CHIKATSU a a Tokyo Deki Uiv., Dept. of Civil Eg., Hatoyama, Saitama, JAPAN - yokoyama@chikatsulab.g.dedai.ac.jp, chikatsu@g.dedai.ac.jp Commissio V, WG V/4 KEY WORDS: Cultural Heritage, Modelig, Visualizatio, Laser scaig, Represetatio, Three-dimesioal, Digital ABSTRACT: Recetly, a laser scaer has bee receivig more attetio as a useful tool for real-time 3D data acquisitio, ad various applicatios such as city modelig, DTM geeratio ad 3D modelig of cultural heritage were proposed. However, 3D Represetatio of historical structures from poit cloud 3D data collected by laser scaer is still issues. I order to reduce the time, labor ad skill for archival recordig of the cultural heritage, the authors discuss measurig system usig 3D scaer ad 3D modelig. This paper describes o 3D represetatio of historical structure usig laser scaer, effectiveess of 3D modelig usig break-lie. 1. INTRODUCTION. 3D REPRESENTATION With respect to recordig work, it is essetial to reduce the amout of time, labor, ad skill employed while makig archival records of a historical structure. I order to do so a laser scaer is used to measure the sites, ad o the basis of the results, a image of the model is costructed. The Triagulated Irregular Network (TIN) model is geerally geerated for 3D modelig usig all the measured poits (HUAN, 1989), however, the large amout of poit cloud data obtaied by the laser scaer pose a problem. Although the Marchig Cube Algorithm (Laurese, Clie,1987) is geerated for reductio of the data values or the polygo umber, the precisio settig of the model chages. This issue becomes a particularly serious problem i accurate 3D modelig. I this paper, the 3D modelig of a historical structure usig a laser scaer ad effectiveess of 3D modelig usig break-lie will be itroduced. Figure 1 shows workflow of certai processes for the 3D represetatio of the historical structure. Measuremet of the Historical Structure usig Terrestrial Laser Scaer Break-Lie Extractio ad Area Divisio usig Break-lie from Measuremet Results (Poit Crowed Data of Terrestrial Laser Scaer) Automatic Corer Poit Detectio by Divided Area 3D modelig usig Detected Corer Poit ad TIN Fig.1 Flowchart for 3D Represetatio Recetly, the laser scaer has attracted cosiderable attetio as measuremet tool that ca perform extesive 3D measuremets i real time. A Total Statio is used for this purpose i same purpose, but it requires a greater amout of labor ad skill. So, the authors measured the historical structure ad carried out 3D modelig

2 usig laser scaers for labor savig. About modelig method, efficiet corer detectio method for 3D poit cloud data was itroduced. Though, effectiveess ad accuracy were ot described. This paper describes about effectiveess ad accuracy of 3D modelig method by efficiet corer detector for 3D poit cloud data. 3. BREAK-LINE EXTRACTION 3.1 Outlie of Modelig Process The historical structure cosists of several flat parts. Therefore, 3D modelig is eabled by the uificatio of all flat parts of historical structure. The break-lies provide the object edges ad ridgelies, ad these ca used to accurately determie the poits to be modeled of the TIN model alog with the flatess classificatio results. The techical outlies are preseted later. 3. Classificatio of Flat ad No-Flat Areas Break-lies are icluded i boudary of flat ad o-flat part. Therefore, Classificatio of the flat ad o-flat area is ecessary before breaklie extractio. I order to classified of flat ad o-flat area, 3D poit cloud data was acquired usig terrestrial laser scaer. A small mask with 30*30 cm area is used istead of the 3D iformatio samples. After a 3*3 poit mask is geerated aroud a iterest poit, the mask size expads to 30*30 cm by computig the plae coordiates for the eighbor poits.the mask is the trasformed so that i the first step, a ormal vector for the mask becomes parallel to the Z-axis (Figure ). Z Y X Fig. Coordiate Trasformatio I the ext step, the stadard deviatio (S.D.) is computed for the iterest poit. The threshold value should be cosidered while classifyig the iterest poits ito flat (groud surface, structure walls, etc.,) ad o-flat areas (trees, bushes, sky, etc.). The threshold value is Z Normal Nor Vector Vect Y X determied o the basis of the measured data, ad it is set as 0.015m(=Rough Upaved Road) i this paper. 3.3 Derivatio of the Break-Lies The break-lies (e.g., object edges, ridge-lies) provide importat morphological iformatio. Although these are idispesable features for DTM geeratio, city ad object modelig, problems with automatic detectio of the breaklies still persist. A techique for automatic detectio of the break-lies usig the flatess values was developed (H.YOKOYAMA, H. CHIKATSU,004). The algorithm is closely related to edge- preservig smoothig, however, oly the flat area is smootheed, ad poits with a larger S.D. withi the o-flat area are emphasized. A small mask was used for smootheig. A mask size of 30*30 cm was sufficiet, ad a iterest poit was smootheed usig the followig equatio: g j = pi gi, j (1) p i where, g j : S.D. for a iterest poit, g i, j ::Differece i S.D. betwee a iterest poit ad its eighborig poits;, defied as g i,j= g i- g j, p i : Weight of the i poit; where the weight is defied as the square of the values betwee the iterest poit ad each eighborig poit. I order to detect the break-lies, smootheig of oly the flat areas is repeated. By repeatig process, the poits with a larger stadard deviatio withi the o-flat areas were emphasized. The break-lies were derived, ad Figure 3 shows detected break-lie for historical structure. Precise break lie that was ecessary for modelig were cofirmed by comparig with maual detected break lies. Figure 4 shows ispectio method of break lie extractio ability. I this paper, the target was measured at the same distace of historical structure (About 10m), ad break lie detectio were doe with chagig thickess of targets (Thickess Rage:0mm-100mm, 5mm pitch). As a result, break-lie was extracted that the target had thickess more tha 40mm. Figure 5 shows Detected Break-lie( Thickess: 40mm).

3 From these results, the aim that could apply proposal techique was cofirmed. (a)rage Image (a) Measuremet Result (Itesity Data) (b) Detected Break-lies by developed method Fig.5 Detected Break-lies 4. EFFICIENT CORNER DETECTOR METHOD (b) Measuremet Result (Rage Data) (c) Detected Break-lies (developed method) (d) Detected Break lies (Maual) Fig.3 Detectio of The Break-Lies for Historical Structure Laser Scaer 10m Thickess Chage: 5m-100mm Target Fig.4 Ispectio Method of Break-lie Extractio Ability 4.1 Extractio of the Corer Poit After the detectio of break lie, the shape of each area which was divided by break lie will be obtaied by tracig the outlie of the area. Whe all curve poits o the outlie of the area are used to modelig, large umber of data values are obtaied. Therefore, the curve poits that would be used to desig the model detected by the followig, ad they were detected from the poit cloud data to costitute the area. " Curved poit of the divided area by break lie" is extracted usig the priciple of the ier product used i a vector. Figure 6 ad the equatio shows the ier product. z t b r a r y x Fig.6 Coceptio of Ier Product r r ab = a b cost Iterest Poit () where, a r, b r =Vector, a, b =Magitude(Distace of referece poit ad Iterest poit), t = r ab r = ( Xi Xa)( Xb Xi) + ( Yi Ya)( Yb Yi) + ( Zi Za)( Zb Zi) ( Xi, Yi, Zi) =Positio data of iterest poit, ( Xa, Ya, Za) =Positio data of referece poit

4 Figure 7 shows the agle data of sample flat area. This sample area is take out by maual as a flat part of historical structure. The miimum agle i eighborhood of attetio poit becomes curved poit. Therefore, there are may miimum agle poits as curve poit, ad the curve poit of the divided area ca ot extracted effectively. May curve poits are extracted from the flat area. Therefore, improvemet of extractio method of curve poit is demaded. icluded i flat area. Figure 9(b) shows agle data without the projectio poits, ad characteristic poit as curve poit ca be cofirmed by this result. But, fie chages which cosist of ifluece of error are icluded i this data, yet. To detect oly characteristic poits automatically, oly the characteristic poits that space agles suddely decrease should be extracted efficietly. For automatic detectio of characteristic poits, authors otices that ope chagig likes resembled a two-dimesioal sigal wave patter, ad low level passage filter which ca remove small wave patter ad detect big wave patter from two-dimesioal sigal wave was used. (a) Target Area (Itesity Image) (b) Sample Flat Area (Itesity Image) Deg Poit (c) Data Fig.7 Data of Sample Flat Area 4. Evaluatio of Projectio Poit Historical Structure's roof is cosists by stems of plat. I classificatio of the flat ad o-flat areas, coordiate trasformatio usig ormal vector is doe. A projectio poit is out from flat area after coordiate trasformatio, though, the poit is classified as flat area by surroudigs. Whe this situatio was cosidered, the projectio poit removal will be performed by attetio poit removal that is ot (a) Curve Poit (Maually Detect) o Itesity Image Deg Poit (b) Data without Projectio Poit Fig.9 Calculatio Result after Projectio Poit Removal Samplig poit umber were limited o usig a low level passage filter, FIR (limited reply aalysis) filter which could make the low level passage filter which ca set mask size was used i this paper. Equatio 3 shows defiitio fuctio of a FIR filter, ad parameter α for quatities of decremet are set for which was determied by experiece.

5 h Where, ~ = g w 0, L, > L (3) h = Filter Coefficiet, L+1=Filter Size ~ g g = 0, L, > L =-,-1,0,1,,, c = iterceptio agular frequecy s = specime agular frequecy w = I 0 ( α 1 ( / L) ) I 0 ( α) 0 1 πω c g = si π ωs, L, > L I 0 (x)= first-class trasformatio Bessel fuctio of first Deg. (a) FIR Filter Poit Poit (b) DOG Fig.10 Detected Characteristic Poits Figure10(a) shows detected characteristic poits by FIR filter. Removal of little chage of agle data were cofirmed from this result, ad characteristic poits are detected automatically by searchig the smallest agle (peak value) from the data. O the other had, DOG (Differece of Gauss) fuctio is applied to the ew agle data, ad the result which was calculated as 0 will be chaged ito 180 degrees as o-curve poit. Equatio 4 shows DOG fuctio ad figure 10(b) shows the detected characteristic poits usig DOG fuctio. 1 x DOG(x)= exp * f ( x) dx πσ 1 σ 1 I compariso with a result of FIR, result of DOG is icludig a lot of o-characteristic poits. As a result, the effectiveess of preseted method is cofirmed. 5. 3D MODELING 5.1 TIN Model usig Corer Poits TIN Model ca make by usig the detected characteristic poits (curve poits). By correspodece i the curve poits ad origial image, textured model will be made. Figure 1 shows 3D model with a flat area. About the textured model makig, camera calibratio was doe beforehad, ad automatic correspodece is doe with the result. 1 x exp * f ( x) dx πσ σ (4) (a) Detected Curve Poit Where, DOG(x)=Result of DOG fuctio, x=distace from otice poit to referece poit, f(x)=, 6 1 = Filter Size, = 1 *1.6 (b) TIN Model Fig.1 3D Model with a Flat Area

6 5. 3D Modelig of Historical Structure 3D Modelig for the "Megro residece" was ivestigated i this paper as oe of applicatios usig break-lie. The Megro residece was costructed i 1797 (about 00 years ago). Figure 13 shows 3D model obtaied by measuremet results. Table 3 shows compariso result about file size of 3D model as DXF file. MCA (Marchig Cube Algorithm, Laurese, Clie,1987) was proposed as 3D modelig method for extractig iterestig parts i CT (Computer Tomography) ad MR (Magetic Resoace) image. By usig preset method, file size was greatly reduced i compariso with MCA. Table3 Compariso result about file size Modelig Method File Size Preset Method 1MB MCA 10MB (a) Origial Image (b) Wire Frame Model usig a measuremet result 6. CONCLUSION The most remarkable poits as results of this approach are its ability to file size of 3D modelig ad accuracy at historical structure. As for further additioal results of this ivestigatio, utility of break-lie to aother targets ca be archived. Therefore, we'll cotiue examiatio about automatic uificatio method of coordiate systems ad effective modelig method. (c) Textured Model usig multiple measuremet results (Frot Image) (d) Textured Model usig multiple measuremet results (Diagoal Course Image) REFERENCES 1.HUAN Y-P, 1989, Triagular irregular etwork geeratio ad topographical modelig, Computer i idustry, pp w.e.laurese, H.E.Clie,1987, Marchig Cubes: A High Resolutio 3D Surface Costructio Algorithm, Proc. ACM SIGGRAPH 87,ACM Press,pp Brugelma, R., 000. Automatic breaklie Detectio from sairbore Laser Rage Data. IAPRS, Vol.XXXIII, Part B3, Amsterdam, The Netherlads, pp C.B.Joes, 1994, The implicit triagulated irregular etwork ad multi-scale spatial databeses, Computer Joural, Vol.37,No.1, pp H.YOKOYAMA, H. CHIKATSU,004,Efficiet Corer Detector for 3D poit Crowd Data ad applicatio to 3D modelig of Structures,Proceedigs of Electric Imagig Sciece ad Techology "Videometrics VIII", Vol.5013, pp Fig.13 3D model usig Obtaied by Measuremet Results