Automation in Construction 22 (2012) 298–305

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Automation in Construction

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Automatic processing of Terrestrial Laser Scanning data of building façades

Joaquín Martínez a,⁎, Alex Soria-Medina b, Pedro Arias a, Alzir Felippe Buffara-Antunes b

a Natural Resources Department, Mining School, Univerity of Vigo, Rua Maxwell S/N, 36310 Vigo, Spainb Geomatics Department, Federal University of Parana, PO. Box 19.001, 81.531-990, Curitiba, Parana, Brazil

⁎ Corresponding author.E-mail addresses: joaquin.martinez.sanchez@gmail.c

asmedina@ufpr.br (A. Soria-Medina), parias@uvigo.es ((A.F. Buffara-Antunes).

0926-5805/$ – see front matter © 2011 Elsevier B.V. Alldoi:10.1016/j.autcon.2011.09.005

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 16 September 2011Available online 9 November 2011

Keywords:Terrestrial Laser ScannerBuilding measurementSegmentationBorder extractionFaçade

Feature extraction on façades from unstructured point clouds is a challenging work, especially in the presence ofnoise. Point cloud segmentation is one of the most important steps in this context. In this paper, a new approachfor automatic processing of façade laser scanner data is introduced. Scanner orientation is partially knownthrough the inclination sensors of the laser scanner used. Knowing these values allows us to reduce the pointcloud data into a profile distribution function. After orientation, this distribution is a series of peaks and valleyssuitable for segmentation. Each segmented layer is afterwards processed to find the façade contours. The resultsobtained prove that the approachmay be successfully employed in building segmentation and extraction of pla-nar features. Moreover, the accuracy of contours is very dependent on the resolution of the scan data.

om (J. Martínez),P. Arias), felipe@ufpr.br

rights reserved.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

The integration of laser measuring devices with photogrammetryis proposed in [1,2] for the inventory of buildings under construction.In recent years great progress has been made in terms of accuracy andspeed in order to obtain and render 3D models of buildings andfaçades. The automatic extraction of different features on a façade isa fundamental problem in point cloud processing. Segmentation ofdifferent façade features can be helpful for localization, classificationand feature extraction. Automatic processing of point cloud data re-sults in highly versatile systems that allow the automation of repeti-tive processes for the acquisition of massive data on large sites [3,4].

In this sense, different approaches for segmentation are suggestedin the literature. They differ mainly in the criteria used for similaritymeasurement within a given set of points and, hence, to carry outdecisions about point clustering. The accuracy of the segmentationis strongly linked with the segmentation method. There are segmen-tation methods that have a large number of parameters, whose mean-ing and effect on final segmentation are not always clear. Most of thecomparisons used separate iterative optimization methods to find thebest set of parameters which describe the feature of the façade. How-ever, many techniques applied to photogrammetric, computer visionand signal processing fields have been used for classification and seg-mentation of the point clouds resulting from TLS [5]. Some of thesetechniques include transformations into a parameter space, such asHough transform and Gaussian sphere. In [6], the authors try to

bring together common elements based on the surface parametersand normal surface information respectively. Techniques such as re-gion growing have been applied to segmented data based on localizedinformation [7,8]. Morphological approaches such as medial axis andskeletonisation have also been used by introducing diffusion equa-tions, radial basis function and grass-fire techniques [9,10]. Accordingto [11], the reliability and accuracy of façade models generated fromterrestrial data depend on data quality in terms of coverage, resolu-tion and accuracy. Façade parts for which only little or inaccurate3D information is available, cannot be reconstructed at all or requiremanual pre- or post-processing. When time-consuming user interac-tion is to be avoided, automatic modeling algorithms which can han-dle heterogeneous data are a solution.

In this paper, a new approach to automatic segmentation of TLSpoint clouds is introduced. The main aim of this work is to extract aset of features of building façades in different layers based on planarfeatures. The first step is the orientation of the point cloud by usingthe RANSAC (RAndom SAmple and Consensus) [12] paradigm. Afterthat, in the following step the segmentation is performed using a pro-file distribution of data and local maxima and minima information.Finally, façade contour points are labeled. These contour points aresuitable for façade measurement and may be exported to CAD soft-ware in order to be annotated. A council museum in Vigo was chosento test the process. “Pazo Quiñones de León”, dates from 1670 and is anexample of urban renaissance palace. This building was chosen as acase study due to the presence of planar features on its façade.

2. Related work

Segments are geometrically continuous elements of a surface orobjects that have some similarities. Segmentation is the process inwhich points that have similar features on a surface are labeled as

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belonging to a segment. A homogeneous segment is characterized byits homogeneities based on certain feature, such as geometric proper-ties, reflectance i.e. intensity data and spectral properties. This is animportant step in the creation of model documentation from 3Dpoint cloud. Various algorithms are proposed in the literature forlaser point cloud segmentation and contour extraction. The methodsof segmentation which are described below are based on geometriccriteria while in Section 2.2 some methods for contour extractionare introduced.

2.1. Segmentation methods

The most relevant methods investigated for the proposal ofdetecting and extracting features from point cloud laser scanningdata are explained in the next sections, and are divided in 3 maingroups: clustering of features, region growing and fitting modelmethods.

2.1.1. Segmentation based on clustering of featuresThe methods based on clustering of features are useful to identify

homogeneous patterns in point cloud data. This process firstly iden-tifies patterns in the data based on attribute and data clusters group-ing. Afterwards, the points belonging to each cluster are labeled as aunique segment in the space object. The results of this technique de-pend on the selected features. This technique has been found verysensitive to the noise data and influenced by the definition of neigh-borhood. An example of a segmentation algorithm using this tech-nique is described below. In [13], a clustering algorithm using anunsupervised classification technique is presented for extractinghomogeneous segments in Airborne Laser Scanning (ALS) data fromunorganized point cloud containing only a limited amount of infor-mation (x, y, z). The authors defined seven dimensional vectors foreach point, that are coordinate position, the parameters of the fittedplane to the neighborhood and the relative difference height betweenthe points and its neighbors. Then, the feature space is clustered usingunsupervised techniques to identify surface classes. After extractingthe surface classes, the points are grouped in object space using spa-tial proximity measurement.

2.1.2. Segmentation based on region growingIn this process, the algorithm starts at a chosen point and grows

around the neighboring points based on certain similarity criteria. InTLS data for façade segmentation the region algorithm is used to ex-tract planar surfaces. It starts by determining a group of nearbypoints. If a plane is found to fit the points within some predefinedthreshold, then this plane is accepted as a seed surface. Afterwards,seed surfaces grow according to specific criteria.. As an example,neighboring points may be added to the surface if they meet somepredefined threshold. After addition of new points to the surface,the equation of the plane is updated. Several extensions for surfacegrowing methods have been suggested. The authors in [9], present avariation of a region-growing algorithm for ALS data. A triangulatedirregular network — TIN is used to describe the basic elements ofthe surface. The merging of triangular elements is carried out bycomparing the plane equation of neighboring triangles. In [14], asegmentation method for ALS data based on region growing is pro-posed in which the normal vector at each point is estimated usingthe k-nearest points. In the growing step, neighboring points areadded to the segment based on criteria of similarity in normal vec-tors, distance to the growing plane and distance to the currentpoint. A method to segment industrial scenes based on a smoothnessconstraint is proposed in [6]. Local surface normal estimation andthe region-growing method are used. The residuals in a plane fittingare employed to approximate the local surface curvature. The grow-ing of segments is performed by using previously estimated pointnormals and their residuals. Points are added to the segment by

enforcing proximity and surface smoothness criteria. The authorsin [8], proposed an approach to automatically extract planar surfacesfrom TLS point clouds following the region-growing segmentationmethod in [6]. In this approach, several parameters need to be spec-ified for the planar surface-growing algorithm, such as the number ofseeds, the surface-growing radius and the maximum distance betweensurfaces. Using different values for these parameters, it is easy to obtainbad segmentation such as over-segmentation, under-segmentation andno segmentation.

2.1.3. Segmentation based on fitting modelThis method is based on the observation that man-made objects

can be decomposed into geometric primitive features like planes, cyl-inders or spheres. This process tries to fit primitive shapes in the pointcloud data to describe the form of building façades. One of themethods that use fitting models is the RANdom SAmple Consensus(RANSAC). The RANSAC algorithm is used to detect mathematicalfeatures like straight lines, circles and plane. Its principle is fullyexplained in [12]. While the RANSAC algorithm has the great advan-tage of being robust, even in the presence of noise, there are alsoshortcomings which should not be overlooked like the appearing ofspurious surfaces, especially in the case of parallel planar surfaceslike stairs. This problem is solved in [15] by sequentially applying al-gorithm to data. The principle of RANSAC algorithm entails the searchof the best plane among a 3D point cloud. At the same time, it reducesthe number of iterations, even if the number of points is very large.For this purpose, it randomly selects three points and calculates theparameters of the corresponding plane. Then it detects all points ofthe original cloud belonging to the calculated plane, according to agiven threshold. Afterwards, it repeats these procedures N times; ineach, it compares the obtained result with the last saved one. If thenew results is better than the last one it is replaced. To find the fea-tures the RANSAC needs four input data (1) the 3D point cloudwhich is a matrix of three coordinate columns X, Y and Z, (2) the tol-erance threshold of distance between the chosen plane and the otherpoints. Its value is related to the altimetric accuracy of the point cloud,(3) the foreseeable is the maximum probable number of points be-longing to the same plane. It is deduced from the point density andthe maximum foreseeable plane surface (4). The probability α is theminimum probability of finding at least one good set of observationsin N trials. According to [16], it lies usually between 0.90 and 0.99.

2.2. Façade contour extraction

Contour extraction may be performed from the segmented planarfeatures. Usually, this operation is used to construct a vector model ofthe building façades and to export this model to computer aided designsoftware. Some research has been conducted to extract the contoursusing an algorithm based on Delaunay triangulation. These works usethe lengths of the edges of the triangulated network toautomatically extract the outer and inner contours of façades. Thethreshold value to extract the contours must be higher than the resolu-tion of the point clouds used [16,17,4]. Following a different method,the authors in [18], use the Triangular Irregular Network (TIN) to de-fine the segment walls. Only long TIN edges appear at the outer bound-ary (wall outline) or inner boundary (holes) of a wall. Boundary pointsare just the end points of the long TIN edges. The geometric reconstruc-tion can be seen as a process of polygon fitting plus the generation ofknowledge based assumptions for occluded parts. Polygon fitting isachieved by directly applying least squares fitting, the Quick hull meth-od, or the Hough transform to extracted feature segments.

3. Methodology

In this section, the methodology used for the processing of terres-trial laser scanner (TLS) data is presented. The laser scanner used is a

Fig. 1. Coordinate systems for façade processing: scanner own coordinate system (SOCS),up-right coordinate system (UP) and final oriented coordinate system (F). The last coordi-nate system is parallel to the façade.

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time-of-flight (TOF) Riegl LMS-390i, which is a hybrid laser scanner.This means that the scanning is unrestricted in the horizontal move-ment with a 360° field of view (HFOV) and has a restricted verticalfield of view (VFOV) of 80°. Some technical data from Riegl specifica-tions are shown in Table 1.

Façade data processing is divided in four main procedures: dataacquisition, data orientation, data cleaning and segmentation and,finally, contour extraction. These last three procedures are fully auto-mated and performed with no need of user intervention. In the fol-lowing subsections three coordinate systems are mentioned: thescanner own coordinate system, the up-right coordinate system andthe final oriented coordinate system, of which YZ plane is parallelwith the façade. These coordinate systems are depicted in Fig. 1.

3.1. Data acquisition

One key feature of the Riegl LMS-390i TLS is the fact that it isequipped with inclination sensors. These inclination sensors permitus to obtain an up-right point cloud of the façade to be processed.The measurement of the sensors ranges from −5° to +5° and willonly provide valid values in the alignments labeled by the manufac-turer as standard and lay back. In this work, we assume that thelaser scanner is set in standard (vertical up-right position) alignmentand near vertical. Thus, rotation angle values about X and Y axes areknown and we can transform the scanner own coordinate system tothe up-right coordinate system.

In data acquisition, the angular step width of a façade scanning isdefined as the angular interval between two consecutive measure-ments of the laser scanner. If the distance between the laser scannerand the façade is known, we can easily transform the angular valueof the laser scanner into a distance step width interval δ(mm). Thequality [19] in the detection of an object with minimum size D(mm) is:

Q ¼ 1− δD: ð1Þ

Given that the distance step width and the minimum size are bothdistances, Q is dimensionless. The Q value is a measure of data qualityand indicates the level to which the object has been scanned. Nega-tive values of Qwould be considered an unacceptable fit whereas pos-itive values would show the percentage confidence that the objectwill be detectable. Decreasing the sample interval will lead to amore dense point cloud in which smaller objects are easier to detectbut heavier to process. For every façade, three successive scanningswith approximate step widths of 100, 50 and 10 mm have beenperformed.

3.2. Data orientation

The façades of historic building to be processed are mainly com-posed of planar features, therefore, orientation is achieved by usingthe planar information of the façade. The orientation plane is com-puted using the RANSAC algorithm [12]. The only unknown angle to

Table 1Some technical data from Riegl LMS-390i specifications.

Max angular resolution 0.002°Measurement range 1–400 mAccuracy 6 mm (one sigma at 50 m)Repeatability 4 mm (one sigma at 50 m)Laser beam divergence (full angle, 1

e2 value) typ. 0.3 mradInclination sensors precision 0.05°

transform the points in the up-right coordinate system to the final co-ordinate system is a Z axis rotation angle.

In order to calculate the proper Z rotation angle, the histogram ofthe X coordinates is computed, obtaining a simple 1D signal with theprofiles of the point cloud. The bin width of the histogram is set toδ=5mm, because this value is similar to the precision of the laserscanner distance meter. In case we had a perfect plane, an orientedprofile would tend to be a Dirac delta distribution and an unorientedprofile would tend to be a constant distribution. Thus, the profile dis-tribution function gives us an idea of how much oriented to thefaçade we are. In Fig. 2 the difference between unoriented and orient-ed profile distribution is shown.

Laser data are processed creating a histogram that is followed by athreshold in order to filter data. After this thresholding, only the binswith the larger number of points are processed. Next step consists ofplane extraction using the RANSAC algorithm. In the up-right coordi-nate system, the equation of the plane is simplified:

A ⋅xþ B ⋅yþ C ¼ 0 ð2Þ

where (A,B,0) is the normal vector of the plane. In this situation, therotation matrix RZ is given by the expression

RZ ¼

AffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA2 þ B2

p BffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA2 þ B2

p 0

− BffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA2 þ B2

p AffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA2 þ B2

p 0

0 0 1

2666664

3777775: ð3Þ

The oriented coordinates of data are known by applying the rota-tion matrix RZ. This orientation procedure is repeated iteratively untilthe rotation angle is lower than a threshold. In this case, the thresholdis set to the value of the precision of the inclination sensors of theRiegl TLS in Table 1, i.e. 0.05°. The work flow to get the façade orient-ed is shown in Fig. 3.

3.3. Data cleaning and segmentation

The next automatic procedure is data cleaning and segmentation.After point cloud orientation is completed, the profile distribution is aseries of peaks and valleys suitable for segmentation by finding localmaxima and minima. Data are filtered for non valid point reduction,by applying a gate depending on the distance of the X coordinatesof the points to the X coordinate of the maximum of the profile distri-bution. In our tests, this filter performed the deletion of the pointsmore than 2 m in front and behind the façade. This threshold dependson the façade, thus, if a façade is more than 4 m long, this threshold

Fig. 2. Comparison of the orientation of a scanning with 100 mm resolution. The unoriented profile distribution (left) of the X coordinates of points is a general random distribution.The oriented profile (right) distribution tends to be a Dirac's distribution.

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should be changed. Fig. 4 shows the oriented profile distribution ofFig. 2 after applying the cleaning filter.

After orientation, laser data is segmented into different classesthat share some common characteristics. In this presentation suchclasses are defined as layers. These layers are obtained using thelocal maxima and minima from the profile distribution. Firstly,local maxima and minima coordinates are filtered in order to rejectconsecutive maxima or minima to obtain a series of consecutivemaximum–minimum pairs. Segmentation layers are placed at eachmaximum position and are limited by the two adjacent minima.Fig. 5 shows a close-up image of the profile distribution functionoverlaid with the series of maxima and minima that define the seg-mented layers. Usually, a great number of layers may be found,hence a filter that reduces the number of layers was developed.This filter takes into account the statistical information of eachlayer derived from the profile distribution and selects only layers

RANSAC plane extraction

RAW DATA

Laser data profiling

Profile Thresholding

Angle< Threshold ?

Point Cloud Orientation

Fig. 3. Workflow of façade segmen

that have less points than the thirtieth percentile. Next, we look forthe successive layers that can be grouped together to form a new dis-tribution of layers. This algorithm is repeated until there are nochanges in the distribution. In Fig. 6 the final segmentation layersare shown. The limits of the segmentation layers are scaled by its rel-ative frequency.

3.4. Contour extraction

The final automatic procedure in this work is the extraction of thecontours of the façade as final result. The contours of the façade areobtained by processing each segmented layer as shown in the workflow in Fig. 3. The layer processing consists of three steps:

1. Point cloud triangulation. The layer points are orthogonally pro-jected to the plane X=Ml, being Ml the X coordinate of the local

Detail layer? Facade extraction

Detail contour extraction

Export contours

SEGMENTED LAYERS

tation and contour extraction.

Fig. 4. Oriented profile distribution of Fig. 1 filtered by X distance gate. The differentlayers in the scanning data are visible in the profile distribution. Fig. 6. Profile distribution after grouping of layers. The star values are the limits of the

layers scaled by its relative frequency.

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maximum of the layer, and triangulated using a 2D Delaunaytriangulation.

2. The triangulation is filtered to remove the edges of the triangula-tion which are higher than a threshold. As a result, the layer issplit in a group of polygons to be processed for border extraction.

3. Labeling and, optionally, refining the border points of each trian-gulation polygon as contour points of the layer.

The threshold of point 2 and the border extraction in point 3 de-pend on the properties of the layer. We define a detail layer as alayer that contains several separate objects (such as windows ordoor objects) and a façade layer (wall) as a layer that contains a fewobjects that fill most of the layer. We classify each layer dependingon its point density. Let us define the point density of a layer as theratio

dl ¼Nl

Alð4Þ

where Al is the area of the convex hull of the layer (m2) and Nl is thenumber of points in the layer. Layers with lower density ratio are la-beled as detail layer whereas layers with higher point density ratioare labeled as façade layer. If a layer is labeled as a façade, a lower

Fig. 5. Oriented Profile distribution of Fig. 1. The segmentation layers are centered atmaxima point (negative star values) and limited by the two adjacent minima (positivestar valued).

number of objects is expected to form the contour of the façade.The point cloud is decimated to a resolution of δd=300 mm inorder to speed up the process of finding out a rough version of thelayer contours in the layer, while preserving the confidence givenby Eq. (1) to find objects larger than 1 m. This rough version is seg-mented in different polygonal objects which are individually refinedand processed in detail to arrive at the final results. However, if alayer is labeled as a detail layer, the threshold of the edge length inthe triangulation is set to two times the resolution of the scanningδ, in order to obtain as many details of the border as possible. If alayer is misclassified as detail or façade layer, a speed down of theprocess is produced, especially in a façade layer misclassified as detail.

The above steps can be summarized in the following algorithm,written in pseudo code:

IMPORT 3d point data

repeatLaser data PROFILINGProfile THRESHOLDINGPLANE extractionPoint cloud ORIENTATION

until orientation angleb inclination sensor precisionfor each layer

if layer is a façadeDECIMATE layerTRIANGULATE decimated layerSPLIT the decimated layer in polygonsfor each polygon

REFINE the polygon in original layerLABEL the contour points

end forelse

TRIANGULATE layerLABEL the contour points

Table 2Some properties of the laser scanning of “Quiñones de León” façade using Riegl LMS-390i TLS.

Scanning linear approx. step width 100 mm 50 mm 10 mmScanning angular resolution deg 0.135 0.068 0.014Scanning time 25″ 1′42″ 19′48″Number of points of scan data 57,949 236,622 5,888,344

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end ifend for

EXPORT contour points

4. Experiments and results

The historic building that was chosen to test the process is the“Pazo Quiñones de León” that is a council museum in Vigo [20]. Thebuilding dates from 1670 when it was constructed after the formerbuilding was destroyed during the war. The building is an exampleof an urban Renaissance palace and is in the style of a main buildingflanked by two towers. In the late nineteenth and early twentiethcenturies the building was renovated by the Marques de Alcedowho donated the building to the people of Vigo in 1924. The buildingwas scanned with approximate step widths of 100, 50 and 10 mm asmentioned in Section 3 from an approximate distance of 40 m. InTable 2 some data from the scanning process are shown. This build-ing was chosen as a case study due to the presence of planar featureson its façade. All of the system procedures were implemented inMatlab. Point cloud data were exported as ascii files from and toRiegl Riscan Pro software that was used with presentation purposes.The processing time, refers to the execution on a PC with a Core i5CPU.

4.1. Segmentation process

The segmentation process also comprises the orientation andcleaning of scan data. In Fig. 7 the result of the segmentation processfor the scan of 10 mm resolution overlayed with the original scanningis shown. For the scan with 100 mm resolution a total of 19 layerswere created, whereas 21 layers were created in the processing of50 mm and 10 mm scans. The distribution of the layers for the scansof 50 mm and 10 mm was very similar: the maximum distance be-tween the locations of the layers is 21 mm. The processing time forthe scans of 100 mm, 50 mm and 10 mm was respectively of 12.84″,33.44″, and 125.28″. In Fig. 8, the final result of segmentation isshown.

Fig. 7. Comparison of the original scan data (rotated on th

4.2. Façade contour extraction

In order to test the validity of the façade contours, the façade wasmanually measured using a Leica TCR1102 total station as a refer-ence. A total of 81 distinguishable points on the borders of the façadewere measured to form a set of 45 distances on the façade. These dis-tances were compared with those measured from the extracted con-tour points. In Fig. 9 the results of contour extraction from the 10 mmscan are shown, showing some of the measured distances for testing.When comparing errors in measurements, it is necessary to take intoaccount the restrictions implied by Eq. (1) and the different dataprocessing steps. Among the processing steps, two of the procedureswere particularly important. Firstly, orthogonal projection of pointsin contour extraction was especially important, given that it is theonly procedure that affects the actual point position directly. Sec-ondly, the final accuracy of the measurements was affected by theorientation process, given that errors in the orientation angles, in-troduced a scale factor to the measurements. In Table 3 some statis-tics of the errors are shown. As expected , there were minor errors asthe step width is finer. The processing time for extracting the façadecontours of the scans with 100 mm, 50 mm and 10 mm stepwidth was respectively 20″, 71″, and about 2400″. The amount of in-formation to be processed with very fine scans is evident with theselast data.

5. Conclusions

In this paper a simple and effective approach to the segmentationand processing of buildings façades data is presented. This research isbased on planar information on the façade and is therefore suitablefor characterizing building façades. Vertical orientation of the scansis achieved through the angles provided by the inclination sensorsof the Riegl laser scanner. The unknown angle is obtained automati-cally using RANSAC. 3D point cloud data are processed in order to ob-tain an unidimensional profile distribution function that permits theorientation , cleaning and segmentation of data. As a first step, themain plane of the façade is extracted using the RANSAC paradigmand the coordinate system is rotated in order to make the X coordi-nate axis perpendicular to this plane. A threshold for façade depth is

e left) and the oriented and segmented scan (right).

Fig. 8. Results of segmentation. Different colors show the different achieved layers in the façade.

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established in order to automatically remove points outside the fa-çade. Automatic segmentation of façade points is achieved by usinglocal maxima and minima in the profile distribution function andstatistical layer grouping. Segmentation results are satisfactory andsimilar results are obtained for different scanning resolutions, hav-ing a maximum difference of 21 mm between the layer maximumposition. Point cloud segmentation in different layers simplifies fur-ther data processing. In this research layers are used for automaticfaçade contour extraction. The quality and accuracy of contour

Fig. 9. Results of contour plotting and

extraction are absolutely dependent on the scanning sample width,because borders are high frequency zones in which step width is es-sential. For a 10 mm step width scanning, a mean error of 7 mm isobtained when compared to total station measurements. The errorstandard deviation is 19 mm. Processing time for the Matlab imple-mentation is acceptable, although for very dense point clouds a na-tive implementation should be considered as a future work. Futurework will focus on solving the segmentation on more complex build-ing façades, on determining the threshold to generalize parameters

annotation with a CAD software.

Table 3Errors in the measurements of the façade contours for the different scanning stepwidth.

Scanning linear approx. step width 10 mm 50 mm 100 mmMean error (m) 0.007 0.035 0.083Standard deviation (m) 0.019 0.043 0.074

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and automatically export geometric primitives to computer aideddesign software.

Acknowledgments

Thanks to CAPES Foundation,Ministry of Education of Brazil (Processcode 0064-10-6), the Regional Ministry of Industry of the Xunta deGalicia (Isabel Barreto Human Resources Program exp. 114) and theMinistry of Science and Innovation of Spain (Project code BIA2009-08012) for the financial support.

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