Intelligent Digital Built Heritage Models: An Approach from Image

Processing and Building Information Modelling Technology

Pedro V. V. de Paiva, Camila K. Cogima, Eloisa Dezen-Kempter,

Marco A. G. de Carvalho and Lucas R. Cerqueira

School of Technology, University of Campinas, Limeira, S˜ao Paulo, Brazil

Keywords:

BIM, Built Heritage, Image Segmentation, Region Growing, Point Cloud.

Abstract:

Conservation and maintenance of historic buildings have exceptional requirements and need a detailed diag-

nosis and an accurate as-is documentation. This paper reports the use of Unmanned Aerial Vehicle (UAV) im-

agery to create an Intelligent Digital Built Heritage Model (IDBHM) based on Building Information Modeling

(BIM) technology. Our work outlines a model-driven approach based on UAV data acquisition, photogram-

metry, post-processing and segmentation of point clouds to promote partial automation of BIM modeling pro-

cess. The methodology proposed was applied to a historical building facade located in Brazil. A qualitative

and quantitative assessment of the proposed segmentation method was undertaken through the comparison be-

tween segmented clusters and as-designed documents, also as between point clouds and ground control points.

An accurate and detailed parametric IDBHM was created from high-resolution Dense Surface Model (DSM).

This Model can improve conservation and rehabilitation works. The results demonstrate that the proposed ap-

proach yields good results in terms of effectiveness in the clusters segmentation, compared to the as-designed

model.

1 INTRODUCTION

Historic buildings have exceptional maintenance,

conservation, and restoration requirements, and be-

cause of this a detailed diagnosis and documentation

are mandatory to the conservation, restoration and re-

habilitation works. Recent decades have enhanced

the consideration of the architectural object as a doc-

ument itself, that contain lots of information. This

knowledge is necessary for the understanding of the

building in its physical reality, historical and cultural

circumstances that have generated and transformed it.

Currently, the results of multidisciplinary surveys

are dispersed on many platforms, making it difﬁcult

to understand the architectural object. On the other

hand, new information and communication technolo-

gies still do little to beneﬁt historic buildings. Intel-

ligent models created by Building Information Mod-

elling (BIM) technologies introduce promising appli-

cation on this issue.

The BIM-enabled method provides a component-

oriented systematic central database gathering shape,

structure, materials, historical and life cycle prop-

erties, a beneﬁt features for cultural heritage ﬁeld

((Penttil¨a et al., 2007; Fai et al., 2011; Manferdini and

Remondino, 2012)). Concerning to the historical as-

sets, the difﬁculty to obtain both the as-designed and

the as-built reference documentation makes the sur-

veying starting from the real object, in a reverse engi-

neering process. Thus, the ﬁrst step for the creation of

an as-is BIM model is data capture. In the data collec-

tion process for as-built/as-is documentation, choos-

ing the best method for reality capture has proven

to be challenging to address(Dezen-Kempter et al.,

2015). High-resolution data capture, such terrestrial

3D laser scanning, lead to expensive equipment, ardu-

ous management, and professional post-processing,

restricting the use of this technology. Photogram-

metry can represent a low-cost technological alterna-

tive for the DSM (Dense Surface Model) generation.

The use of high-resolution cameras coupled to UAV

(Unmanned Aerial Vehicles), in high precision sur-

veys, has been increasingly frequent, with results very

close to 3D laser scanning. This paper describes a

BIM-driven approach for historic buildings, based on

(i) photogrammetric 3D building survey with a UAV,

(ii) image processing and segmentation into architec-

tural components and (iii) development of BIM as-is

model-based.

This paper is organized as follows: Section 2

558

Paiva, P., Cogima, C., Dezen-Kempter, E., Carvalho, M. and Cerqueira, L.

Intelligent Digital Built Heritage Models: An Approach from Image Processing and Building Information Modelling Technology.

DOI: 10.5220/0006615005580565

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages

558-565

ISBN: 978-989-758-290-5

presents related works. Section 3 describes the pro-

posed approach. Section 4 describes equipment used

and details about the method developed in this re-

search. Section 5 presents the results from the indi-

vidual stages. Finally, in the Section 6 shows conclu-

sions and future works guidance.

2 RELATED WORKS

Many researchers have tried to construct as-is BIMs

adopting various input types, also considering differ-

ent levels of building information in their models (Lu

and Lee, 2017). Although laser scanning is still the

acquisition method mainly used, photography and its

integrations are already accepted as a feasible and

promising low-cost alternative, especially Structure

from Motion point clouds (Bhatla et al., 2012).

Brilakis et al. proposedan information-richmodel

by merging data from laser and image collections

(Brilakis et al., 2010), this fusion can be reached ap-

plying supervised IPC algorithm then smooth with

scale-invariant feature transform. Once combined a

textured 3D surface its estimated by mesh triangula-

tion and pixel correlation. Even without implementa-

tion, authors recommend feature extraction from ge-

ometry and image canonical parts (Savarese and Fei-

Fei, 2007) as descriptors for item labeling. The work

deﬁned a complete as-is BIM as an open challenge.

A semi-automatic approach for existing buildings

was proposed by Dore and Murphy, also integrating

laser and image data. The concept called Historic

Building Information Modelling (HBIM) provides a

parametric object library using geometric program-

ming language (Dore and Murphy, 2014). A col-

lection of predeﬁned architectural elements its used

to simplify manual modelling tasks. Pre-processing

steps, such registration, and ﬁltering are carried out

ﬁrst. Although experiments indicate more efﬁciency

than existing BIM tools, pattern limitation, and low

automation reduces usability and remain the manual

effort necessary to process the information.

Another alternative proposed by Jung et al tries to

increase automation for modelling in scenes captured

with laser scanning. Rather than use point clouds,

researchers suggest simple edge lines as guides for

object shaping in BIM software (Jung et al., 2014).

Borders are carried out segmenting data into sub-

sets sorted has planes. Subdivisions are made using

RANSAC (Fischler and Bolles, 1981) that calculates

the probability of a point belongs to the best group,

followed by reﬁnement. Thus, each plane is projected

onto 2D binary images where non-zero represents oc-

cupied positions. Boundary tracing search for not null

pixels in value changing areas and iteratively connect

neighbors. Despite the fact that this method adds au-

tonomous stages the process appear semi-auto indeed,

and hard to evaluate.

Wang et al have a similar approach, comparing

with Jung et al, differing only in algorithm chose and

BIM integration. Same sensors where applied on the

acquisition (Wang et al., 2015), but data suffered ef-

ﬁciency enhancement as downsizing and outliers re-

motion. Hereafter,region growing its used to segment

planes using curvature and angular similarity between

points. Once disjoint, all areas have its edge com-

puted by concavehull border extractor (De Berg et al.,

2008). Instead of ﬁnish the process as soon as lines

are delimited, previouslydeﬁned rules classify the ob-

tained forms, adding likely labels. This method was

evaluated comparing its output with golden standard

and reached high error rates.

The technique described in our paper, analog to

Wang et al, uses region growing algorithm to iden-

tify architectural elements and similar pre-processing.

Our contribution can be enumerated as follow: (1) a

new UAV acquisition protocol is presented, specify-

ing close-range methodology able to produce viable

images for a 3D model generation; (2) project docu-

ments are used to evaluate the segmentation stage in-

troducing trustworthy validation; (3) clouds acquired

by laser scanning and images were compared and ﬁ-

nally (4) every step, since acquisition until model ver-

iﬁcation, were profoundly presented and produced a

replicable workﬂow.

3 PROPOSED APPROACH

This research applies a few methods described in the

scientiﬁc literature for each one of the process steps

of the IDBHM creation. It was required adjustments

in peculiar characteristics of the case study. Figure 1

shows the proposed methodology approach.

The method is composed of six primally stage.

Those phases can be generalized as:

Capture Planning: The use of UAV in the capture

of elements in short and close-range domain, partic-

ularly in the case of historical heritage, still does not

have techniques widely disseminated, which leads to

the application of strategies created for terrestrial cap-

ture stations (Nex and Remondino, 2014). Particular

challenges in speciﬁc structures require a case to case

adaptation. Despite that, three parameters are the con-

sensus among different conventions: (i) calibration of

all sensors involved, (ii) the full angular range of cap-

ture and (iii) the high percentage of overlap between

images.

Intelligent Digital Built Heritage Models: An Approach from Image Processing and Building Information Modelling Technology

559

Figure 1: Methodology description of each step in the work-

ﬂow.

Acquisition: Based on ﬂight planning, the geomet-

ric object information was captured using UAV and

modeled applying proprietary DSM generator.

Preprocessing: Several challenges are related to the

point cloud segmentation task, such as noise ﬁltering,

low density and non-organized data (Nguyen and Le,

2013). In order to minimize such problems ﬁltering

operations or other kind of data preparation are usu-

ally necessary.

Segmentation: Segmentation is the process of par-

titioning an image changing its representation into

something more meaningful. Even though its im-

portance in image analysis and decades of research,

segmentation remains a challenger task. The tridi-

mensional case is found in the same statement, hard

in the same way. Low angular variability is a well-

known property of plane surfaces. This property is

used for point cloud segmentation techniques like re-

gion growing, for instance.

BIM Modelling: The output of point cloud segmen-

tation was a set of points from each building archi-

tectural element (clusters). The clusters enabled the

development of the BIM Model corresponding to the

development of parametric objects.

Evaluation: Quantitative metrics are toke based

on comparisons between clusters, as-designed docu-

ment, and point cloud accuracy.

4 METHODOLOGY

To validate the proposed approach, a 19th-century

historical building in the city of Limeira (Brazil), the

Boa Morte Church (BMC), was selected as the case

study. The church was designed by the Italian Aur

elio

Civatti in 1867 in neo-classical style. The build-

ing technique used in the BMC was rammed earth, a

feature from traditional Portuguese construction that

uses mud and clay mixture woven together with wood

and bamboo structure. The great thickness of the

walls is a characteristic feature of this technique. The

BMC main body walls are between 90 and 177cm

thick.

Figure 2: [a] Original facade of BM’s church (1867); [b]

Facade after restoration (1890).

The facade collapsed only 12 years after its com-

pletion. In 1890, part of the Frontispiece was redone,

replacing the rammed earth for baked clay brick.The

modenature of the main door and the choir windows

were also modiﬁed, like the triangular neo-classical

pediment by an arched one of eclectic characteristics

(Figure 2). The main architectural elements of the fa-

cade were identiﬁed, aiming at the segmentation pro-

cess and the subsequent modelling of BIM parametric

objects. Figure 3 highlights these elements:

The following subsections present the detailing of

each step of the proposed workﬂow.

4.1 Capture Planning

The implementation of BMC, in the center of a

square, foster the UAV imagery capture because there

is no signiﬁcative obstacle in the 15m in front of its

facade. This way, the UAV could ﬂy freely, both verti-

cally and horizontally, operated either by application

or manually (Figure 4). For the UAV scanning plan

was considered the distance between the building and

the UAV, the building height and width (25m× 25m),

the minimum desired overlapping(70%) and the cam-

era performance. Thus, was proposed a ﬂight with six

horizontal lines, starting at the top of the building, the

camera automatically snapped the shot every 2 sec-

onds with the UAV ﬂying at about 5m/s.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

560

Figure 3: BMC architectural elements: (1) right bell tower;

(2) left bell tower; (A) Pinnacle with with a golden weather-

cock); (B) octagonal dome; (C) triangular pediment of the

bell tower; (D) oculus; (3) double pediment with a clock on

the tympanum; (E) rampant scrolls; (F) straight cymatium;

(4) frontispiece, with spare bell towers base (cornice high-

lighted in the ﬁgure made up of cymatium and friezes); (G)

chorus windows, entirely open, in full arch; f. oculus; (H)

main door with lintel in segmental arch and door frame both

in carved stone;(I) basement.

Figure 4: UAV positions for aerial photography, [a] eleva-

tion - vertical view and [b] cross section - horizontal view.

4.2 Acquisition

The following approach was taken (see ﬁgure 5):

• Improve georeferencing precision by placing

UAV in high altitude, that enables a large number

of satellites synchronization.

• Start object capture drawing a curved path, cover-

ing elevation ranges.

• Repeat previous steps changing sensor angle.

Our method combines elements from Murtiyoso et

al, who established perpendicular ﬂight succeeded by

four others, changing sensor angulation in 45

◦

(bot-

tom, up, left and right) (Murtiyoso et al., 2016). Point

clouds created using curve path instead of straight

lines provides higher angle covering resulting in pre-

cise models.

The equipment used was Inspire 2 of DJI equipped

with noise reduction camera Zenmuse X4S with

8.8mm/F2.8−11 ﬁeld of view 84

◦

, 20MP 11.6 Stops

resolution and 3-Axis Gimbal.

In facade acquirement, 369 photos were taken and

used to create a point cloud adopting proprietary soft-

ware (Pix4D, AutoDesk Recap, and AutoDesk Re-

make). A better outcome was shown by Pix4D, offer-

ing performance and unlimited input images. Specif-

ically, 368 images were used (only one of then have

was eliminated during software calibration), creating

a point cloud containing 106 million densiﬁed 3D

points with 14.286,2 average density per m

4.3 Preprocessing

In the sequence, we list the preprocessing tasks used

in this work.

Figure 5: Flight examples with sensor angular adjustment.

Perpendicular and oblique highlights.

• Parameterized Cut: It is possible to deﬁne a spe-

ciﬁc area, the target area, and excluding some

regions of the point cloud (Alvarado, 2015),

(Janssen, 2017). We deﬁne a limited volume in

the point cloud and consider only the points in-

side it.

• Downsizing: We deﬁne a set of cubes as voxels

representing the 3D space. For all points inside

a voxel, it is considered only one, the central el-

ement (Moravec, 1996), as representative of the

point cloud. The goal is decreasing the high num-

ber of points and the density of the point cloud

(Wang et al., 2015).

Noise Filtering: The scanning process using a

UAV usually include incorrect points, generat-

ing data deformation (Mitra and Nguyen, 2003).

An intuitive way to interpret points classiﬁeds as

noise is like outliers. In this work, we apply

an outliers ﬁltering technique proposed in (Rusu

et al., 2008), based on the average and standard

deviation in the neighborhood of a point, accord-

Intelligent Digital Built Heritage Models: An Approach from Image Processing and Building Information Modelling Technology

561

ing to the Equation 1.

∗

= {p

∗

∈ P | (µ

− α · σ

) ≤ d ≤ (µ

+ α · σ

)}

(1)

where P

∗

represents all points in the point cloud;

∗

is a subset in the neighborhood q; µ and σ are

the mean and standard deviation on radius k.

4.4 Segmentation

Region growing algorithm initially developed for

shape classiﬁcation in images (Besl and Jain, 1988),

divides point cloud into related subsets separated by

surface changing (Rabbani et al., 2006).

Simpliﬁed algorithm (Rusu et al., 2008) is de-

scribed in following steps:

• Let p

be any point belonging to a set of points

= {p

, p

,..., p

}, a number of near points,

equivalent to the regional descriptors, are deﬁned

following the equation:

− p

≤ (1+ ε) · |q

− p

, (2)

where q

is the neighbor inclusion radius limited

by 1+ ε at the moment x.

• Once deﬁned neighborhood, point correlation is

used to estimate their normal vector (angle be-

tween surface and a common point, usually sensor

position).

• Analogous, mean local curvature is calculated.

Before point operation, an evaluation on each nor-

mal vector~n is computed by:

arccos(h~n,~n

i) ≤ θ

, (3)

if the difference between ~n and ~n

is less than θ

threshold, points belong to the same region. Curva-

ture checking is used for smooth evaluating surface

likeness, also restricted by a parametric limit.

4.5 BIM Modelling

The clusters point cloud data was exported in OBJ for-

mat to be read in the BIM software. We used REVIT

(Autodesk) to model the facade, and the software con-

verts the point cloud raw formats into RCP (REVIT

native format).

Although REVIT can index the point cloud in the

Project Environment, the Revit Family Editor, where

is created parametric elements (door, windows, wall

moldings), does not support point cloud raw formats.

It was necessary to convert the clusters OBJ format

with Meshlab (open source software) into DXF for-

mat supported by REVIT Family Editor Environment.

Each BIM component was labeled using the

OminiClass element, which is primitive of RE-

VIT and later adjusting the analytical structure of

the project to the classiﬁcation system proposed by

ABNT NBR 15.965 (based on OminiClass). Figure 6

depicts the ﬁnal model of BMC facade with the point

cloud cluster of the frontispiece highlighted in color

(by lift), and the Family Model (parametric object) of

the Front Door.

(a) (b)

Figure 6: BIM models of BMC shows [a] facade and [b]

main door.

4.6 Evaluation

We evaluate the clusters obtained in the segmentation

step then assess the point acquisition by comparing

the spatial coordinates of control points. Firstly, we

carry out the qualitative evaluation through precision,

recall and accuracy metrics computation. This task

considers segmented and as-designed images. Sec-

ondly, we deﬁne control points over the as-designed

document and compare its spatial coordinates (or the

difference) among those in the point cloud and mea-

sured by a Total Station.

We use the accuracy metric, which is calculated

about the as-designed model. Accuracy, denoted by

A, is calculated according to the Equation 6; Preci-

sion and Recall area given in Equations 4 and 5, re-

spectively (van Rijsbergen, 1979).

Precision =

TP+ FP

(4)

Recall =

TP+ FN

(5)

A =

TP+ TN

TP+ TN + FP+ FN

(6)

where True-Positive (TP) and True-Negative (TN) are

the pixels belonging or not to the reference and re-

sulting images, respectively; False-Positive (FP) and

False-Negative (FN) represents pixels belonging ei-

ther to the reference image or the resulting image, but

not to both. These parameters create the confusion

matrix.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

562

5 EXPERIMENTS AND RESULTS

In this section, we ﬁrst introduce the dataset, the

results of the proposed approach and method limita-

tions.

Dataset. The results built upon the data acquired

from the scan of the BMC, accomplished by a UAV.

Initially, the point cloud had about 18 million points.

Point Cloud Library

functions were used, as well as

dedicated codes developed in Python.

Results and Discussions. After the preprocessing

step explained in Section 4.3, the point cloud has now

about 900 thousand points. Figure 7(b) illustrates the

resulting image after the segmentation step using a

region growing algorithm. Note that it was possi-

ble to divide the original image, Figure 7(a), in dif-

ferent clusters, representing the architectural compo-

nents: this is an essential task to create a model of the

scanned image.

(a)

(b)

Figure 7: (a) Original and (b) resulting image after segmen-

tation (architectural elements represented by color).

Table 1 shows the analysis of the proposed seg-

mentation step for seven different architectural com-

ponents. The clusters resulting from the segmentation

stage are projected into 2D images before the compar-

ison. This task is accomplished utilizing orthographic

projection and density resizing reduction using mor-

phological operations (Gonzalez and Woods, 2002).

Usually, clusters are mainly ﬂats, since the prop-

erty of the region growing algorithm uses the sur-

face curvature information of the point cloud. The

corresponding image as-designed is compared pixel-

to-pixel according to adopted metrics. Figure 8 il-

lustrates this comparison for two distinct compo-

nents, pediment, and frontispiece: on the left, the as-

designed images and, on the right, the clusters result-

ing from the proposed approach.

To check the accuracy of the acquired points by

the UAV, we deﬁne some Control Points (CP) using

http://pointclouds.org

(a) Pediment (tympanum and cymatium)

(b) Frontispiece voids of windows and doors

Figure 8: As-design elements vs cluster projections.

a Total Station

. Figure 9 shows the CP deﬁned to

the facade of the Boa Morte Church. This CP was

comparedto the points obtained by the UAV and those

from the as-designed model.

Figure 9: Control Points (CP) deﬁnition, in red.

Table 2 shows the analysis of distance between

different control points using a Total Station and data

of the as-designed Model and point cloud. It is pos-

sible to observe the excellent performance of the ap-

proach and acquired data. The absolute deviation of

the point cloud is between +0,06 m to −0,75 m,

while for the as-designed model this range is from

−0,12 m to +1,12 m. Also, the average deviation is

−0,11m to the point cloud, or −0,04 m if we ignore

the maximum difference (control points 2 and 14).

The photographic acquisition using the UAV, and

the reconstruction employing software, provides a

high precision 3D representation of the building. Its

accuracy can be compared to others works, as re-

ported by (Bayram et al., 2015), which presented a re-

lationship among 3D laser scanning and photogram-

metric reconstruction.

DSM models made from point cloud has non-

structured features as lack of topology and semantic

TS650 model FOIF, angular precision 1s, linear preci-

sion 2mm+ 2p.p.m.

Intelligent Digital Built Heritage Models: An Approach from Image Processing and Building Information Modelling Technology

563

Table 1: Analysis of segmented regions compared to the as-designed model.

COMPONENTS PRECISION RECALL ACCURACY

Frontispiece with voids of windows and doorways 87,7 93,4 85,8

Tower base (right) 94,6 86,6 84,8

Tower base (left) 94,3 94,7 90,9

Right Bell Tower 85,3 89,5 83,4

Left Bell Tower 84,4 63,9 69,5

Doble pediment (tympanum and cymatium) 55,7 39,3 82,3

Scrolled pediment 96,0 87,2 91,2

Table 2: Comparison analysis of distance (in meters) between Total Station (TS), as-designed model (AD) and Point Cloud

(PC).

CONTROL POINTS TS AD PC TS− PC AD− TS

1-2 0,99 1,00 1,00 -0,01 0,01

1-3 4,69 5,20 4,88 -0,19 0,51

2-5 10,17 10,26 10,14 0,03 0,09

2-14 22,88 24,00 23,63 -0,75 1,12

6-7 3,02 3,08 3,12 -0,10 0,06

8-9 1,59 1,93 1,65 -0,06 0,34

9-10 2,09 1,97 2,15 -0,06 -0,12

10-11 1,41 1,53 1,46 -0,05 0,12

13-14 0,97 1,00 0,96 0,01 0,03

2-15 18,16 18,52 18,10 0,06 0,36

discretization. However, the IDBHM created by

BIM methodology, as we presented in this work, can

provide a reliable documentation of the building,

mainly the usual necessary information of historical

heritage.

Limitations. The primary limitation of this work re-

sides on its non-automated phase. Besides signiﬁcant

time reduction on pre-processing, segmentation and

evaluation, steps as acquisition and modelling still

need laboring work. Another weak point is the use of

proprietary black-box software to create point clouds,

which increase inter-operational dependence.

6 CONCLUSIONS

Our approach is organized in six steps, as follows:(i)

capture planning; (ii) acquisition; (iii) preprocessing;

(iv) point cloud segmentation; (v) 3D semantic model

creation and (vi) evaluation.

We presented two main contributions, related to

the data acquisition protocol and the evaluation of the

image segmentation method. We proposed an evalua-

tion model based on the confusion matrix and metrics

widely used in the literature: precision, recall, and ac-

curacy. These parameters provide useful information

about the quality of resulting image obtained after the

segmentation task. The comparison is accomplished

between cluster image (segmented image from point

cloud data) and component image (from the architec-

tural project). Another relevant contribution was the

validation step proposed in this work. This task com-

pares points acquired by the UAV with those obtained

by a Total Station and the container in the as-designed

model.

The results obtained indicates a satisfactory per-

formance of the cluster segmentation step, according

to precision, recall and accuracy metrics. However,

the values of these metrics are inﬂuenced by the ex-

istence of small holes in the clusters generated due

to data acquisition failures. Also, we are investigat-

ing whether the projection technique used in the com-

parison as-designed model vs cluster projection could

decrease the quantitative analysis performance.

In future work, we would like to improve the data

acquisition process, deﬁning a UAV ﬂight protocol.

This protocol must provide a comprehensive angular

point range and high overlap rate. Besides, we also

intend to implement and test different image segmen-

tation techniques, to obtain information-rich clusters

useful for further pattern recognition steps.

ACKNOWLEDGEMENTS

This work was supported by S˜ao Paulo Research

Foundation (FAPESP), grant 2016/04991 − 0 and

2017/02787−9. We acknowledge Prof. Mauro Men-

zori and Prof. Vitor E. Molina Jr. by their collabora-

tion with Total Station equipment. Special thank for

Prof. Vitor R. Coluci for his advices. We also thank

our reviewers for their helpful comments.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

564

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