=Paper=
{{Paper
|id=Vol-2394/paper033
|storemode=property
|title=Towards Registration of Construction Drawings to Building Information Models using Knowledge-based Extended Geometric Hashing
|pdfUrl=https://ceur-ws.org/Vol-2394/paper33.pdf
|volume=Vol-2394
|authors=Maciej Trzeciak,André Borrmann
|dblpUrl=https://dblp.org/rec/conf/egice/TrzeciakB18
}}
==Towards Registration of Construction Drawings to Building Information Models using Knowledge-based Extended Geometric Hashing==
https://ceur-ws.org/Vol-2394/paper33.pdf
Towards Registration of Construction Drawings to Building Information
Models using Knowledge-based Extended Geometric Hashing
Maciej Trzeciak, André Borrmann
Technical University of Munich, Germany
maciej.trzeciak@tum.de
Despite the increasing adoption of BIM technologies, construction drawings are still excessively
used in the industry. Many leading AEC figures predict the coexistence of 3D models and 2D
drawings for at least a decade. Very often, these two are managed – and in particular for the more
advanced design stages – developed separately from each other which puts their consistency at
severe risk. In current practice, the deviation detection between drawings and 3D models is a manual,
cumbersome, and erroneous process. To overcome this issue, the need for automation emerges. In
this paper, we explore this problem and propose a robust drawing-to-model registration system
which provides a basis for subsequent consistency verification. It translates the 2D-3D matching
problem into the 2D-2D one by cutting a 3D model into sections and measuring similarity between
them and the construction drawing. At the core of the proposed approach is geometric hashing,
which we extend to further automate the registration process. We propose to build two geometric
hash tables which are being matched against each other while measuring similarity. In order to
reduce the matching ambiguity – which is the drawback of the proposed extension – we estimate the
minimal number of bases to be randomly sampled in the presence of outliers by using the random
sample consensus. Moreover, we decrease the burden of building hash tables by reducing the bases
– in which features are described – to line segments. We demonstrate the applicability of the
proposed system in a case study and provide experimental results.
1. Introduction
The construction industry is currently in a transition period in which drawing-based practices
are slowly being replaced by model-based ones, which implies the coexistence of construction
drawings and digital 3D models. According to many leading figures in the AEC industry, the
intense use of technical drawings in the design and construction of buildings will continue for
at least a decade despite the increasing adoption of BIM technologies. In particular, drawings
will prevail for a number of years as the legally binding documents for the actual construction
processes on site, but also for approval processes by permission authorities. This is also
reflected by the recently published international standard ISO 19650 that defines the concept
of information containers composed of models and the associated drawings (ISO, 2018). Most
of the exchange information requirements (EIR) documents and the associated contracts
demand the consistence of the models and the drawings handed over at a certain point in time
(data drop) (ISO/DIS, n.d.). In many cases, checking the consistency is part of the approval
processes for passing one of the quality gates (Preidel et al., 2017). However currently, these
checks must be done manually as no automated methods exist.
Commercial BIM design applications allow to derive drawings that are fully consistent with the
created BIM model. However, due to the established processes of the industry, construction
drawings and BIM models are subsequently managed separately from each other. Even worse,
in many cases they are developed further and refined independently from one another.
Therefore, the need for automated verification of consistency between BIM models and the
corresponding construction drawings persists. This paper does not question the well-
documented value of model-based design and construction (Sacks et al., 2019). However, it
respects the reality of the coexistence of models and drawings for many years to come. More
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specifically, it connects to today’s established working practices where drawings form the
major basis of information exchange, even if BIM models are created by individual
stakeholders. In this paper, we introduce a robust method for the registration of construction
drawings to BIM models, which provides the basis for consistency verification and deviation
detection.
In a broader sense, shape registration is a task of aligning two shapes in a shared coordinate
system (Shao et al., 2011). More specifically, the positioning of a 3D model against an image
is known as the model-to-image registration (Jung et al., 2016), co-registration (Avbelj et al.,
2015), or 3D-to-2D registration (Wunsch and Hirzinger, 1996). Generally, most registration
problems can be divided into three steps: (1) Feature extraction – where distinctive features are
derived from two objects to be matched; (2) Matching – which measures the degree of similarity
between the two sets of extracted features and (if necessary) establishes the explicit
correspondences between them; (3) Transformation – which transforms the matched features
so that they align in a shared coordinate system; The researches related to the matching step
focus mainly on either feature-based shape matching or matching topology.
We propose a registration system, tailored specifically to the drawing-to-model matching
problem in the construction industry, which focuses on feature-based shape matching. The idea
of this approach is similar to the one used in view-based 3D model retrieval domain (Tangelder
and Veltkamp, 2008; Liu, 2012), in which two 3D models are defined to be similar if they look
similar from different views based on the shape of the object in these views. Accordingly, the
2D-3D matching problem is translated into the 2D-2D one.
The ultimate objective of our method is that a given drawing (subsequently called a query
drawing) is supposed to be placed (translated, rotated, scaled) in the three dimensional space of
a BIM model such that the cross-section derived at the corresponding plane (subsequently
called a model section) is the most similar to the query drawing. Before this, however, we derive
model sections and measure their level of similarity to the query drawing by comparing the
extracted features. For the most similar model section, we establish explicit correspondences to
the query drawing and align the drawing to the model section.
At the core of the proposed matching algorithm is geometric hashing. We decrease the burden
of building hash tables by reducing the space of bases to line segments. Furthermore, we
propose to extend the original geometric hashing algorithm in such a way that the matching
process is fully automated. To this end, we propose to build two geometric hash tables which
are being matched against each other while measuring similarity between a model section and
the query drawing. In order to reduce the matching ambiguity – which is the drawback of the
proposed extension – we propose to estimate the minimal number of bases to be randomly
sampled in the presence of outliers by using the random sample consensus - RANSAC (Fischler
and Bolles, 1981).
The paper has the following structure. Section 2 describes our model-to-drawing registration
system, including feature extraction, similarity measure and matching methods, correspondence
establishment, and alignment. Section 3 contains a case study for a standard 3-story residential
building along with results. We conclude this paper with Section 4 devoted to discussion.
2. Drawing-to-model registration system – methodology
The drawing-to-model registration system focuses on feature-based shape matching and uses
knowledge-based extended geometric hashing as the method for measuring similarity and
correspondence establishment between a query drawing and a set of cross-sections (in this
2
paper, we name them model-sections) derived from a 3D BIM model. The overview of these
steps is shown in Figure 1. We will walk through them in detail in the next sections.
Figure 1: High-level pipeline for the drawing-to-model registration system.
2.1 Generation of model sections
We translate the 2D-to-3D matching problem into 2D-to-2D by cutting the 3D model. In this
paper, we use horizontal planes in such a way, that each of them is located above each building
level so that it reflects the engineering practice of deriving floor plans at 1.20 m. above a
building level. Accordingly, we reduce the number of potential model sections (the
discretization of solution space) to be compared with the query drawing significantly. In case
of the drawings showing the vertical cross-section of a building, we propose to cut the 3D model
vertically and at uniformly distributed distances.
(a) (b) (c)
Figure 2: In our system, the 2D-to-3D matching problem is translated into 2D-to-2D by cutting a BIM
model (a) using planes as shown in (b). As a result, the solution space is reduced to separate 2D model
sections (c).
In case of the drawings of facades, we propose to create orthographic projections of all the sides
of the BIM model. Moreover, since BIM models are comprised both of geometry and semantics,
we are able to select the types of building elements that we use for matching. For example,
elements as doors and windows are shown in construction drawings as icons conforming to
engineering standards and not as their pure orthographic projections, and as such, they are not
good candidates for the shape-based assessment of similarity by our system.
As shown in Figure 3, the shapes of a floor plan and a model section derived from the same
level appear rather similar to a human, however they inevitably differ from each other in details.
The purpose of the left one is to convey engineering design and therefore, they are richer in
explicit and additional descriptions (hatch patterns, fills, dimensional lines, descriptions) than
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BIM models, mainly due to conforming to engineering standards. For example, the building
elements such as thermal insulation, concrete columns or walls cut by a plane are filled in with
hatch patterns, explicit dimensional lines are drawn, the symbols of certain types of building
elements such as doors or windows are agreed icons and differ from their orthographic
projections. In addition, what might not be seen instantly from Figure 3 is the fact that the
orientation and the scale of the presented drawing and model section are already correct. In a
real scenario, the descriptor responsible for measuring similarity must be invariant to rotation,
translation and scaling if this process is supposed to be automated and the correspondence
properly established. Moreover, construction drawings very often contain additional details not
present in BIM models due to the fact that the modeling of all details in 3D is not yet sufficiently
efficient.
Figure 3: Visual comparison of a construction drawing and its pure section derived from an IFC model
at the same level.
Having this in mind, the proposed concept is to measure the commonality of construction
drawings and the model sections. The more they have in common, the more similar they are.
We extract a unique fingerprint (a unique set of features) occurring in each model section and
we find this unique set in the query drawing. In other words, we find the common part of the
two sets of features – one set belonging to a model section and the other belonging to the query
drawing. We repeat this process for each model section, and the section with the greatest
common part is then proposed as the best match. It is then used for the subsequent explicit
establishment of correspondence and the alignment.
2.2 Feature extraction
Since the model sections and the floor plan are in vector-based representations, we query them
in such a way that we extract their features and segment lines. We do not use methods for
detection of key-points or lines from raster images (e.g. Hough line transform).
In building engineering, corner points are the dominant type of features since construction
drawings are mainly comprised of straight segments. Accordingly, for each derived 2D model
section we extract key-point features as corner points, here understood as the start, end, and
intermediate points defining polylines, and as the start and end points of single segment lines
whose overlapping points (duplications) are later filtered out. Due to the fact that we propose
to build hash tables using segment-wise bases, and not all the possible combinations of bases
of key-point features, we extract a set of line segments described by their start and end points.
In a similar vein, we extract the same key-point features and segment lines from the query
drawing. In the core of the proposed methodology is the measure of the common part between
the model sections and the query drawing. Since the model sections, by definition, contain only
elements cut by a plane, and not projected onto the plane, it makes sense to look for these
elements in the query drawing. In the engineering convention, such lines cut by a plane are the
4
thickest, and therefore, we first query the thickest lines in the query drawing, and based on
them, we extract the key-point features and line segments. The line segments are ordered sets
of points and for each line segment, two bases are later taken into consideration (start-end-point
basis and end-start-point basis), which is necessary for unambiguous alignment in a further step
of the proposed solution.
2.3 Similarity measurement using geometric hashing
In order to find the common part of two sets of features, the descriptor must be able to partially
match objects. By partial matching we mean finding a shape whose part is similar to a part of
another shape, which in this case, translates to finding the matching features between two sets.
Moreover, it must be tailored for specific purpose of matching many model sections to a query
drawing within reasonable time. Accordingly, we do not need descriptors setting up the explicit
correspondence for each model section and the query drawing, for example such as presented
in (Belongie et al., 2002). Additionally, translation, scaling, and rotation invariances are
required simply due to the fact that models and drawings are described in arbitrary coordinate
systems. Therefore, in the proposed approach, there is a need to provide the descriptor which
supports partial matching and provides fast scores for the common part.
We have decided to match shapes between the corresponding building elements in 2D and 3D,
and not – for example – to match the grid systems of drawings and 3D models. From our
experience, grid systems are very rarely exported to IFC models, and therefore, matching based
on the grids would impose a severe prerequisite. Instead, we provide a general solution to the
existing problem which works without major prerequisites and thus, without barriers in its
application.
As such, we propose and extend geometric hashing which was originally developed as an object
recognition technique in computer vision (Wolfson and Rigoutsos, 1997). The general
procedure and ideas behind it can be found in the original paper and we skip this part due to the
page limit. Geometric hashing consists of two stages – preprocessing and recognition.
Preprocessing phase
In our case, the preprocessing stage turns the features described in segment-wise bases extracted
from a query drawing into a hash table. The naïve solution would be to describe the features (𝑛
stands for the number of the features) in all their combinations (𝐶𝑛,𝑘 – combinations without
repetition, with 𝑘 = 2, because a basis consists of an ordered pair of points), which yields the
quadratic function as shown in equation 1. The equation shows asymptotic number of naïve
bases using all combinations of key-point features needed to build a geometric hash table:
𝑛 𝑛! 𝑛(𝑛 − 1)
𝐶𝑛,𝑘 = ( ) = = = 𝑂(𝑛2 ) (1)
𝑘 𝑘! (𝑛 − 𝑘)! 2
We reduce the basis space, however, to segment-wise bases (𝐵𝑚 ), meaning that each straight
segment becomes a basis. This reduces the number of bases in which a hash table is built to a
linear function (see equation 2), as each segment line increases the number of bases by 1 (or in
case of query drawings, each segment line increases the number of bases by 2):
𝐵𝑚 = 1𝑚 = 𝑂(𝑚) (2)
Recognition phase
Since the hash table of the query drawing is built with the features described in all segment-
wise bases, in order to compare the similarity to a model section, we need to sample one
5
corresponding basis in the model section, and count the number of matching features by
accessing entries of the hash table built on the query drawing. In other words, the task comes
down mainly to finding a corresponding segment line between the model section and the query
drawing. The model section whose basis has the most corresponding features (and thus the
greatest common part) is therefore the most similar to the query drawing. In the ideal situation,
this task would be trivial if the query drawing and the model section did not have any outliers
or noise. We could randomly sample a basis, describe all the features in this basis, and the
model section with the highest number of corresponding features would be the most similar to
the query drawing. Our model sections, however, do contain outliers because, for example, they
are composed of the cross-sections of individual building elements (see an example in Figure 4
which shows a part of a model section comprised of two walls marked in green and blue).
Consequently, not all of the line segments between the query drawing and the model section
overlap, and those which do not, are considered as outliers (an outlier can be seen in Figure 4
marked with a yellow arrow, whereas the green arrow shows an inlier). Another example where
outliers occur is a drawing which consists of building elements not modelled in the BIM model,
and therefore not appearing in the derived model sections. Accordingly, there is a need to
estimate how many bases need to be sampled from the model section in order to select at least
one error-free basis corresponding to the query drawing.
Figure 4: Corresponding segment lines between a model section (at the top) and a query drawing (at the
bottom). The presented part of the model section is mainly comprised of two cross-sections of two
building elements marked with the green and blue fills. Using a green arrow, we mark an example of
bases which correspond with each other (inliers), whereas in yellow, we mark an example of bases which
do not correspond with each other (outliers).
The naïve number of the sampled bases would equal to all of the segment-wise bases (as in the
preprocessing stage executed on the query drawing). This, however, would increase the burden
of building another full hash table, and what is worse, it would increase the matching ambiguity
due to redundant bases. In order to reduce these drawbacks, we propose to estimate the minimal
number of bases to be randomly sampled in the presence of outliers by using the random sample
consensus so that at least one inlier is sampled. Having estimated the minimal number of
sampled bases, we describe all the features in these bases and store them in another hash table
which is being matched against the hash table of the query drawing. By doing this, we provide
the full automation of the recognition phase (and thus the automation of the drawing-to-model
registration). The major difference to the original geometric hashing is that the native procedure
urges the user to choose an arbitrary basis in the to-be-recognized image, whereas we propose
to build another hash table by storing the features described in a certain number of sampled
bases (in which there is at least one inlier) estimated by RANSAC, and to match these two hash
tables against each other. As a result, the matching bases between the model section and the
query drawing can be determined.
6
Accordingly, we model the outliers as a random variable, and having assumed a certain outlier
rate (ϵ), we sample k bases with very high probability (p) to select at least one (r) inlier basis.
The number of trials (k) is computed as shown in equation 3 (Fischler and Bolles, 1981).
log(1 − 𝑝)
𝑘= (3)
log(1 − (1 − 𝜖)𝑟 )
2.4 Correspondence establishment
Geometric hashing does not solve for explicit correspondence between all corresponding
features (Belongie et al., 2002). Instead it uses the features to vote for a specific basis. As a
consequence, the explicit correspondence in our proposed system can be automatically
established only between most voted bases from each hash table.
In our modified version, we simultaneously vote for bases in two hash tables. At the end, there
is one basis in each hash table (in case of no redundant bases have been sampled in the
recognition phase) that is most voted and we establish the correspondence between them. We
do not explicitly recover all the feature-to-feature correspondences. Instead, we use the two
corresponding bases between a drawing and a model section in 2D to align them.
2.5 Determination of alignment
Having found two corresponding bases, we align them in the following way: (1) The
corresponding segment lines are viewed as vectors and the difference in scale between a model-
section and the drawing is determined by the quotient of the norms of these vectors; (2) The
translation vector is calculated between the middle points of the corresponding segment lines;
(3) The rotation angle is calculated using the formula for the vector-vector dot product, where
the corresponding segment lines are seen are vectors. Having calculated the above parameters,
the drawing is accordingly scaled, translated and rotated.
3. Test case
We demonstrate the applicability of the proposed system in a case study of a standard 3-story
residential building. Our prototype operates on IFC-based BIM models, and construction
drawings in the DXF format. The geometric hashing algorithm and its extension has been
programmatically implemented from scratch based on the original paper and extended as
described in the previous section. IFC and DXF readers, viewers, and scientific computing
libraries are free-license packages. The visualization of the BIM model and the floor plan can
be seen in Figure 5. The BIM model was created using a commercial BIM modeling tool. The
floor plan was then derived and modified to conform to engineering standards using this tool.
Ultimately, the model and the floor plan were exported to the IFC and the DXF data formats.
We cut the IFC-based BIM model horizontally at 4 different levels (shown in Figure 6a) in such
a way that the horizontal cutting plane is located at 1.20m above each floor level, which
translates to the following building levels: -1.30m, 1.20m, 4.00m and 6.20m. The corresponding
model sections are shown in Figure 6 b-e. The model sections are comprised of all the instances
of parent type IfcProduct. The dimensions of the BIM model (and thus model sections) are
stored in meters, whereas the dimension of the floor plan are stored in millimetres.
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Figure 5: Visualization of a BIM model as a standard 3-story residentail building, and its corresponding
floor plan portraying the ground floor derived at 1.20m level.
In case of the floor plan, we first filter all the geometric primitives, select those with the highest
stroke width and build a geometric hash table for all the extracted key-point features described
in all the extracted bases (segment lines). Next, for each model section, we extract its key-point
features and segment lines and build a geometric hash table. We calculate the number of
randomly sampled bases with the probability for selection at least one error-free base (𝑝 = 0.99)
and outlier rate (𝜖 = 0.9). In such a case, the number of sampled bases is equal to k = 44, which
reduces the number of bases used to build the hash tables around 4-7 times in case of model
sections b, c and d presented in Figure 6. In all the cases, we quantize the coordinates of features
stored in the hash tables by a bin of size 0.24.
(a) (b) (c) (d) (e)
Figure 6: An IFC-based BIM model horrizontally cut at 4 different levels – figure a. Figues from b to e
present the corresponding model sections.
3.1 Experimental results
We match the hash tables of each model section against the hash table of the query drawing. In
Figure 7, we show the 3 most voted bases for each model section. The model section derived at
level +1.20 is most voted, and within this level, there are two bases with a very high score. This
level is also marked in green on the right side of the figure.
Figure 7: Results obtained after matching the geometric hash table of the query drawing against all the
geometric hash tables of the model sections. The first three most voted bases are presented.
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The most voted basis at level +1.20 and the most voted basis from the query drawing are then
proposed for setting up the correspondence, which is shown in Figure 8 on the left hand side.
Figure 8: Left: The two corresponding bases between the query drawing and the most voted model
section. Right: Aligned query drawing and model section.
4. Conclusions and future work
In this paper, we propose a robust feature-based shape matching method for the registration of
construction drawings to BIM models with the extended geometric hashing at its core. We test
this method in a case study of a standard 3-story residential building. The early experimental
results are promising when it comes to measuring similarity with level-wise accuracy between
floor plans in vector-based formats and BIM models using key-point features and segment line
bases. The proposed reduction of basis space to segment lines reduces the burden for building
hash tables due to its linear (and not quadratic) complexity.
The proposed extension of geometric hashing allows to cast votes simultaneously in two hash
tables and therefore, it is possible to automatically establish the correspondence between two
most voted bases. The results show that this method correctly attributes the query drawing to
one of the model sections, and correctly indicates two corresponding bases which allows to
establish the explicit correspondence. As +1.20 level shows, however, the proposed random-
sample-consensus-based sampling of bases for model sections in the presence of outliers
returned 2 inliers (1 redundant basis). Any redundant basis causes the matching ambiguity while
setting up the explicit correspondence between two bases as the margin between the first and
the second most voted bases is very little. In the future, in such situations, we propose to
measure the similarity between the two registered objects by establishing more corresponding
features and calculating the distance between them (for example using the L2 distance
function). Moreover, it must be noted that the more similar the model sections are to the query
drawing (fewer outliers), the more corresponding key-point features can be matched. In our
case, the prototype operates on IFC models, whose pure cross-sections are comprised simply
by cutting individual building elements which make the model section be prone to outliers (in
comparison to the corresponding construction drawing). Additionally, such parameters as the
outlier rate and the bin size need to be tuned on more real projects, as they drive the performance
of this system, which we could not show in this paper due to its page limit.
There are many other cases in which our registration system could be tested as well. Matching
drawings of details which contain elements not necessarily modeled in BIM models (for
example - highly detailed steel connections), matching vertical cross-sections or projections of
facades are use cases which could be tested. In all of these cases, the derivation mechanism for
model sections presented in this paper must be extended so that it returns good 2D candidates
9
for comparison to the query drawing. The derivation mechanism is potentially the biggest
limitation in the proposed system because it is hard-coded for specific cases (horizontal model-
sections for floor plans, vertical models-sections for cross-sections and so on). Also, in case of
drawings which are not comprised of mainly straight segment lines, we would have to extend
the types of features that are supposed to be matched because the fewer key-point features
extracted, the less confident the system is. Therefore, the second major limitation of the
proposed system is its non-consideration of curved geometric primitives which limits the
applicability of this system in certain cases with more advanced geometry.
Overall, the proposed registration system shows promising results in building engineering for
further automation of both the registration process and its subsequent consistency verification
which could be used, for example, in the verification of consistency between construction
drawings and BIM models at quality gates in approval process by authority.
Acknowledgements
This work is supported by ALLPLAN GmbH and Nemetschek Group. The authors would like
to gratefully thank these companies for making this research possible.
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