ONTOLOGY-DRIVEN 3D RECONSTRUCTION OF

ARCHITECTURAL OBJECTS

Christophe Cruz, Franck Marzani

Laboratoire Le2i, UFR Sciences et Techniques, Université de Bourgogne

B.P. 47870, 21078 Dijon Cedex, France

Frank Boochs

Institut i3mainz, am Fachbereich 1 - Geoinformatik und Vermessung

Fachhochschule Mainz, Holzstrasse 3655116 Mainz

Keywords: Scanning, automatic reconstruction, architectural objects, knowledge database detection, semantic,

ontology.

Abstract: This paper presents an ontology-driven 3D architectural reconstruction approach based on the survey with a

3D scanner. This solution is powerful in the field of civil engineering projects to save time during the cost

process estimation. This time is saved using efficient scanning instruments and a fast reconstruction of a

digital mock-up that can be used in specific software. The reconstruction approach considers the three

following issues. How to define an ontology to drive the reconstruction process? How to find semantic

objects in a cloud of points? How to control an algorithm in order to find all objects in the cloud of points?

This paper underlines the solutions found for these questions.

1 INTRODUCTION

In the field of civil engineering projects it is often

difficult to update a building. Most of the time,

information concerning its design has simply

disappeared. Indeed, no process was usually defined

to store digital data concerning the design of the

architectural project. Such data would be helpful to

estimate the update costs. For instance, the security

laws evolve and the buildings have to follow them.

Consequently, the buildings must be updated too.

Also, the building has to be captured “as-built” using

expensive geometrical measurements to improve the

design and to evaluate the update costs. These

measurements have to be done by engineers and

comprise several steps like the establishment of a

geometrical reference and a local data capture. This

process is time consuming, that’s why automatic

algorithms are welcome in order to reduce time and

cost. In principle, photogrammetry and laser

scanning both have the potential for improvements

and higher degrees of automatism. In this article we

focus on a method based on the laser scanning

survey. Digital building plans being defined by the

civil engineers with the help of CAD software

mostly contain simple geometries. In addition,

semantic rules are applied to achieve better design.

However, during various processing steps and their

inevitable data exchange object information is

reduced to a set of vectors using formats like DXF

or DWG. As a consequence, semantic information

and object structures are lost. Such problems might

be avoided with file formats like IFC, defined by the

International Alliance for Interoperability. This

standard associates a semantic definition to

geometrical elements in the field of building

projects. Up to now, this standard is used as an

exchange format by international leaders of CAD

software. This format is of value for "as-built"

problems, aiming at the digital reconstruction of real

buildings. Consequently, it should be helpful to use

the IFC semantic information directly during an “as-

built” reconstruction of a building for an automatic

reconstruction. In this article we focus on a method

not only based on the laser scanning survey and IFC

semantics but also introducing an ontology defining

Cruz C., Marzani F. and Boochs F. (2007).

ONTOLOGY-DRIVEN 3D RECONSTRUCTION OF ARCHITECTURAL OBJECTS.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications, pages 47-54

DOI: 10.5220/0002047300470054

 SciTePress

the semantic context to simplify the automatic

reconstruction.

The following section gives background

information on projects that aim to reconstruct a 3D

model of a building from survey data. In these

projects the semantic information that describes the

context of the building takes an important place.

Section 3 describes our approach inspired from these

projects. Section 4 focuses on this method by

explaining all the important parts of the

reconstruction process.

2 BACKGROUND

Today, computer-driven evaluation of spatial data

sets is limited by the complexity of the objects to be

extracted. As a matter of fact it is complicated and

time consuming to formulate rules in order to detect

and extract objects geometrically correct. It is due to

one essential reason that the objects are broken

down into many small geometrical pieces. Even if

each piece can be treated in an isolated way, it is not

possible to treat all data at one time. Therefore, the

use of knowledge and its introduction into the

process of evaluation is promising for global

interrelations. The impact of semantic information

on the reconstruction process depends on the

structure of the raw data that has to be handled.

Therefore, it is necessary to study those structures

and reconstruction processes. A short survey is

given in the two following subsections. The first

subsection is concerned with reconstruction methods

based on photogrammetric data and the second

considers reconstruction methods based on scanning

data. Each method has its own characteristics and

advantages but the best choice depends on the

material available, the object to be captured, the

required precision, and the time available (Grün,

2002), (Bryan, 1999), (Balletti, 2004), (Boehler,

2004).

2.1 Photogrammetry

Reconstruction methods based on photogrammetric

data are of two kinds. The semi-automatic methods

consist of the interaction with the user during the

whole process. The automatic methods consist in the

initiation of the process by the user at the beginning

so that later the process runs without user

interaction. Semi-automatic reconstruction methods

can be found in the projects: Realise (Zitova, 2003),

TotalCalib (Robert, 1995), (Bougnoux, 1997),

(Faugeras, 1997), Marina (Cantzler, 2002),

(Nüchter, 2003) and Rekon (Frasson, 1999),

(Loscos, 1999), (Poulin, 1998). Automatic

reconstruction methods have been developed by

Pollefeys et al. (Pollefeys, 2000) and Zisserman et

al. (Werner, 2002). They use the projective

geometry on non-calibrated images. Pollefeys'

system combines various algorithms from computer

vision, like projective reconstruction, auto-

calibration and depth map estimation. Of special

interest for our work was the project Aida (Weik,

1996) because it uses a semantic network to guide

the reconstruction. This method opens a new way by

using semantic information. The automatic

reconstruction remains a difficult task in spite of

many years of research (Backer, 1981), (Fleet,

1991), (Grimson, 1981), (Jones, 1992), (Marr,

1979), (McMillan, 1995). The major problems are

the impact of the viewpoint onto the appearance of

the object in the image. This is due to the changes

with respect to geometry, radiometry, occlusions and

the lack of texture. Strong variations of the

viewpoint may destroy the adjacency relations of

points, especially when the object surface shows

considerable geometrical variations. This

dissimilarity causes confusion in the determination

of correspondence and it is worse when partial

occlusions result in a disappearance of object parts.

In cases of weak texture the algorithms do not have

sufficient information to solve the correspondence

problem correctly. Usually, this is the reason why

the reconstruction fails.

2.2 Scanning

Accurate reconstruction of a surface model from

unorganized points of clouds provided by scanning

systems are complex and are still not completely

solved. Problems arise from the fact that the points

are generally not organized, contain noise and do not

reflect directly the object characteristics, for

example. Computer-based processes of object

extraction are therefore limited in their efficiency. F.

Remonido gives a good overview of existing

algorithms (Remondino, 2003). Close attention is

given to the work of Cantzler et al. (Cantzler, 2002)

and to the work of Nüchter et al. (Nüchter, 2003)

because these projects use semantic information.

Planes which are being reconstructed are associated

to a semantic interpretation which has to fit to a

network model (Grau,1997). A tree of

“backtracking” allows to find the best mapping

between the scene interpretation and the semantic

network model. A coherent labelling exists if all

surfaces are labelled.

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

Compared to photogrammetry, problems seem to

be fewer in the field of scanning but an automatic

reconstruction is just as impossible as it is within

image based techniques. One important reason for

this is the complexity of objects in combination with

redundancy, incompleteness and noise within the

clouds of points. Improvements can be expected

when knowledge about the scene is used, as is

shown in the work of Cantzler and Nüchter. This is

the reason why the nature of the geometrical objects

and the existing constraints between them make it

possible to support computer based detection.

3 ONTOLOGY-DRIVEN

RECONSTRUCTION

As the work presented in the previous section shows,

a semantic context may support considerably a 3D

reconstruction. This might be helpful for the

reconstruction within clouds of points where some

elements of the object have already been detected

and need to be combined to a final structure.

Semantic knowledge is also useful for

photogrammetric tasks. This might either help to

group 2D points in the images or to form the spatial

structure when several images are available. The

semantic structure of the spatial object model is the

same, only the use and the interaction with the data

are different. In the following section our vision of

the use of semantic definition for 3D reconstruction

will be sketched. Our main idea is founded on the

duality between context and constraints. It starts

from the idea that it is easier to rebuild a scene using

available knowledge about the scene’s elements.

Therefore, in order to define the knowledge about

the context, a coarse geometrical and semantic

model has to be established. We call this c

oarse

odel “CM” and it is a spatial structure that defines

a building and the semantics about the elements that

compose the building.

Figure 1: Example of an architectural CM.

The “CM” (e.g. fig. 1) defines the rough

geometry and the semantics of the building without

any real measurement. Such a "CM" will then be

updated by means of real measurements representing

the building. In order to achieve this, knowledge has

to represent the real world by reflecting entities and

relations between them. Therefore, knowledge

constitutes a model of the world and agents use their

knowledge as a model of the world. In addition, to

model the semantics of knowledge as well as the

structure where this knowledge is stored, it is

necessary to reach a higher conceptual level. For

that, knowledge representation is independent of

knowledge use. Thus, knowledge representation and

inferential mechanisms are dissociated (Guarino &

al., 1994). On the other hand, domain

conceptualization can be performed without

ambiguity only if a context of use can be given. In

fact, a word or a term can designate two different

concepts depending on the particular context of use

(Bachimont, 2000). The semantic of knowledge is

strongly constrained by the symbolic representation

of computers. Therefore N. Guarino (Guarino, 1994)

introduced an ontological level between the

conceptual level and the epistemological level. The

ontological level forms a bridge between

interpretative semantics in which users interpret

terms and operational semantics in which computers

handle symbols (Dechilly, 2000). Some projects

presented previously have used a semantic network

to model the semantics of a scene. We will use an

ontology language for several reasons.

• First, the implementation of an ontology is a

mapping stage between the system elements and

their ontological “counterparts”. Once this

mapping has been carried out, the representation

of elements in the ontology is regarded as a

meta-data diagram. The role of a meta-data

diagram is double (Amann, 2003). On the one

hand, it represents the knowledge shared on a

domain. On the other hand, it plays the role of a

database schema which is used for the

formulation of requests structured on meta-data

or to constitute views.

• Secondly, the ontologies allow to dissociate

knowledge representation and inferential

mechanisms. We have sketched a generic

definition of semantic elements that permit to

dynamically add new elements in the ontology

without changing the code. Those new elements

are also taken automatically into account in the

storing process and the inferential mechanisms.

• Thirdly, once the “CM” has been corrected,

geometric and semantic information in the

ontology can be exported into an IFC file

format. So, the 3D model can be used directly in

civil engineering processes and CAD software.

ONTOLOGY-DRIVEN 3D RECONSTRUCTION OF ARCHITECTURAL OBJECTS

4 METHOD DEFINITION

Our method aims at developing a solution to

reconstruct automatically a 3D building from a point

cloud measured by a 3D scanner. This solution has

to consider the three following aspects. How to

define a geometric and semantic coarse model? How

to find objects in a cloud of points? Which

algorithms to use as a propagation method to find all

objects in the cloud of points? In our solution the

user has to assign the context by defining a coarse

model of the building to be reconstructed. Then the

user interactively selects a set of points in the cloud

that represents a wall. The selection is also mapped

to the coarse model by assigning the corresponding

wall in the “CM” (e.g. figure 3). Then the user starts

the reconstruction algorithm. Within an iterative

process the plane representing the wall is found and

will be used to correct the model. The process starts

with the mapped plane, corrects it, and continues

with information in “CM” to detect an adjacent

plane by propagation. A final stage should aim at the

detection of smaller parts like doors, windows, etc.

3D Scanner

User DatabaseCoarse Model

File of points

User

First plane

3D reconstruction

algorithm

Mapping

3D Scanner3D Scanner

UserUser DatabaseDatabaseCoarse ModelCoarse Model

File of pointsFile of points

User

First plane

UserUser

First plane

3D reconstruction

algorithm

Mapping

Figure 2: Global view of our method.

The three following subsections give an

overview of our solution to achieve the final goal

consisting of the definition of a “CM”, the plane

detection that allows to find objects in the cloud of

point, the search of objects by propagation

permitting the correction of the “CM”.

4.1 Definition of the “CM”

This section describes our method used to define a

“CM”. With the application that has been developed

(e.g. figure 4) the user can indicate the general

geometrical structure of a building like the position

and the size. Moreover, the interaction with our

application allows to define automatically

constraints between elements of the “CM” which are

described by the architectural ontology. For

instance, a window is a concept that composes the

architectural ontology. This window has a constraint

which is “the window must be in a wall with a

bigger size”. To implement this part we resolved

three main issues. First, it was necessary to define

the structure of the architectural ontology. Secondly,

it was necessary to manage the persistence of data as

well. Thirdly, data should be exported into an IFC

file format. To resolve the first issue, two ways were

available which are the static way and the dynamic

way. The static way consists in implementing

directly the class necessary to describe the elements

that compose a building as well as the relations.

Once the necessary elements are defined, the

conception of the databases and graphical interfaces

can be overtaken.

Figure 3: Selection of a subcloud of points.

The problem linked to the static way arises when

new kinds of objects have to be added to the

ontology. As a result, the database and the graphical

interface must be adapted. The dynamic way

consists in taking into account this issue and in

developing a structure that allows to add a new kind

of object without changing the structure of the

database and the graphical interface. The model

defined in this application takes into account this

issue and manages the description of the classes and

instances from the start.

Our model is divided into two levels which are

the semantic level and the instance level. The

semantic level allows to store the description of the

ontology classes from a OWL (Web Ontology

Language) file. The OWL file is defined with the

help of the software Protégé OWL plugin. The

instance level allows to store the description of the

instances from the classes of the ontology. The

storing process and the graphical interface are then

not modified when a new class has to be added.

Nevertheless, there is still a problem in the

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

management of a dynamic ontology. It is necessary

to manage the positioning interactions between

elements.

Figure 4: Definition of the “CM”.

For instance, if a ground is moved then the

elements carried by this ground, like the walls, must

undergo a displacement. The solution is to define

predefined behaviors and then associate those

behaviors of the future elements to the existing

behaviors. For example, a new class column has the

same behavior as a wall. It is indeed located on the

ground and touches the ceiling. Thus, it was

necessary to locate the types of behavior according

to the possible interactions. A set of behavior was

found but only three of them are described here. The

ground is rather a horizontally flat element and on

this one walls can be deposited. The walls are rather

vertical elements. A window is an element in a wall.

From those facts the types of elements are the

“horizontal elements”, the “vertical elements” and

the “vertical subelements”. Concerning the basic

constraints, the “horizontal elements” are used as

support for the “vertical elements”. So the ”vertical

elements” are positioned on the “horizontal

elements” and contain the “vertical subelements”.

The “vertical subelements” are contained in “vertical

elements”. With the help of those predefined

behaviors and constraints, it is easy to add a new

complex class in the architectural ontology.

Concerning the IFC export, the ontology

contains all information about the object that

composes the building. The architectural concepts

and relations are fully inspired by the IFC standard.

So, the objects are exported by our export module

with geometrical definition and the relations

between them but the constraints are only used for

the validation of the “CM”.

4.2 Plan Detection and Research by

Propagation

The objects which have to be found in the point

cloud are planes. This geometric primitive is the

easiest one to search and also the fastest one

(Remondino, 2003). During the plane search

process, there are several stages that have to be

carried out. The first stage is the partitioning of the

point cloud. When it is known that a set of points

defines only one plane, it is easier to find an

equation of the plane that represents this subset. In

most cases the point clouds do not model only one

plane. To simplify the search of planes in such a

cloud it is helpful to initially cut out such a subset of

points. After a first segmentation is achieved, one

can calculate the plane equation of each subset. But,

the equation of a plane is not sufficient for a wall

because the extensions are not contained. It is thus

necessary to limit the equation of a plane, in order to

represent the edges of the wall. The equation of a

plane provides the orientation of the wall and the

outlines are found in the point cloud.

However, this ideal situation is affected by

several real world factors. Like in all physical

measurements there will always be noise in such

measurements. In addition, the point cloud may

contain environmental objects like trees or traffic

signs partly hiding the real object. Those objects will

add more or less erroneous points that will not

represent the building. Moreover, the wall is not an

ideal mathematical planar object, leading to a

roughness of several millimetres on the surface.

Finally, not all the 3d points will be coplanar

because only in an ideal model the points can be

aligned perfectly.

All these problems must be taken into account in

the detection of planes. The noise, the erroneous

points and irregularities in the wall are parameters

which cannot be modified, and thus it is necessary to

manage them in the program. Another important

point is that the plane detection algorithm must be

automated. The user should not have to interact with

the algorithm and only has to judge that the results

are correct or not.

The degree of complexity increases enormously,

when a simple plane should be detected in a point

cloud representing a complex object. This is why the

algorithm starts with an adjacency search allowing

to group the object into small spatial elements

ONTOLOGY-DRIVEN 3D RECONSTRUCTION OF ARCHITECTURAL OBJECTS

(voxel). All points which are contained in a voxel

are considered as a subgroup and a plane is found

out in each voxel (e.g. figure 5). Subsequently, the

neighbourhood is used to extend the voxel planes.

The size of the voxels is an important parameter. If

the voxel size is too large then multiple planes can

be found in one voxel. Thus, it does not resolve the

problem. If the voxel size is too small then it is hard

to find a correct plane equation.

After initial planes are found, they have to be

extended within the point cloud. This is achieved by

starting from the plane equation for one voxel and

looking at the adjacent voxels if there are points

possibly belonging to the same planar surface part.

There are several methods to support such a

decision. One solution is to calculate a plane for

each voxel by means of “least square adjustement”.

This is relatively simple to set up, but needs to

define a threshold for the different angle of

orientation to define the similarity. A better solution

starts with the voxel having the best residual error

and then it consists in checking the distance to this

plane, beginning with the direct neighbours. If the

sum of the distance is lower than a certain threshold

then the voxels are fused. For the fused group a new

equation has to be calculated in order to refine the

result.

The plane search by propagation is done in an

iterative way. The process starts from a voxel and

looks at the neighbours. When the neighbours check

the same criteria then the process continues with the

"neighbours of the neighbour". Then, all planes that

have been found are checked to determine if there

are similarities between them. The method based on

the angles is also used to avoid useless calculations.

If the angle between two planes is higher than 60°,

then it is not necessary to try to see whether they can

be fused.

The plane detection in a point cloud is the most

delicate part of the process but needs, in addition, to

find the real dimensions of the various elements.

One way to achieve this might calculate a

bounding box by taking the extreme values of the

points. Some turns of this bounding box with a

predefined angle produce acceptable results. In order

to find the correct bounding box the characteristics

of the delimiting points have to be checked, because

single points cannot be regarded as reliable (e.g.

figure 7). Only a set of points allows to minimize the

errors. The most precise results will be generated by

use of the final planes constituting the walls.

Assuming the calculations of the equations were

done with large sets of points and thus of sufficient

accuracy, the edges of the walls can be calculated by

intersecting adjacent planes. The result is much

more precise and avoids the problem of the parasitic

points (e.g. figure 8).

4.3 Correction of the “CM”

The principle of the project is to use a point cloud

coming from a building survey to correct a coarse

model that defines the context. Although the

improvement of the coarse model is the most

interesting result, the initial model - and the

knowledge contained therein - is of basic importance

for the update process. Therefore, two aspects are of

interest in the context of model improvement: first,

readjusting the initial wall definition compared to

the “CM”, and, secondly, the support for the

Found rectangle

Wished

point

Parasitic

poi

Similar

Figure 5: Plane research in voxel space.

Figure 6: Plane similarity between distant voxels.

Figure 7: Bounding box and parasitic point.

Figure 8: Plane intersection.

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

propagation of the plane detection in the whole point

cloud.

Repositioning of the initial plane compared to the

coarse model

At the beginning, the cloud of points can be

positioned in a way completely different compared

to the “CM” coordinate system. For the search of the

other planes, it is fundamental "to readjust the cloud

of points". That readjustment defines an identical

framework that accelerates the process. This

repositioning takes place during the research of the

first plane. Once the readjusted plane has been

accomplished, the wall of the “CM” is corrected.

The correction of this wall is propagated to the

adjacent elements thanks to the constraints defined

in “CM”.

Research of nearby elements and correction

To propagate the “CM” modification a direction was

defined. The propagation is made left towards right

then bottom towards top. The “CM” contains

information of the neighbourhood. Indeed, the

neighbourhood relations are automatically defined

during the “CM” definition. To find the bounding

box of the second element, the equation of the initial

element is used to deduce from “CM” which rotation

is defined between the initial element and the second

element to be treated. The theoretical equation of the

second wall makes it possible to calculate the

distance between the second element and each point

of the cloud. Thus, by leaving an error margin, we

can detect by reading the entire file that contains the

points, all the points which are close to this plane.

Then, the sub cloud of points undergoes a detection

of plane and edges described in the preceding

section. Thanks to this information the second

element is corrected. Once all the elements from the

“CM” are corrected, the sub elements contained in

elements of the building must be corrected with the

same methods of search of plane and correction.

5 CONCLUSION

This paper presented a solution for the 3D

reconstruction driven by an architectural ontology.

At this time, most of the huge issues were resolved

and the complete process was prototyped. The

following issue to be resolved is the use of the other

primitives like the cylinder to reconstruct

automatically more complex scenes. Furthermore,

we are also working on a solution to reuse a partial

“CM” that allows to define more easily a complex

“CM”.

ACKNOWLEDGEMENTS

Authors would like to thank Jeremy Contant,

Laurent Huot and Nicolas Montelle for their

important contribution on the application

instanciation.

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