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A constrained growth method for procedural floor plan generation

Authors:
A CONSTRAINED GROWTH METHOD
FOR PROCEDURAL FLOOR PLAN GENERATION
Ricardo Lopes1, Tim Tutenel1, Ruben M. Smelik2, Klaas Jan de Kraker2, and Rafael Bidarra1
1Computer Graphics Group, Delft University of Technology, Delft, the Netherlands
2Modelling, Simulation and Gaming Department, TNO, The Hague, the Netherlands
KEYWORDS
Virtual worlds, procedural modeling, floor plan genera-
tion
ABSTRACT
Modern games often feature highly detailed urban envi-
ronments. However, buildings are typically represented
only by their fa¸cade, because of the excessive costs it
would entail to manually model all interiors. Although
automated procedural techniques for building interiors
exist, their application is to date limited. One of the rea-
sons for this is because designers have too little control
over the resulting room topology. Also, generated floor
plans do not always adhere to the necessary consistency
constraints, such as reachability and connectivity. In
this paper, we propose a novel and flexible technique for
generating procedural floor plans subject to user-defined
constraints. We demonstrate its versatility, by showing
generated floor plans for different classes of buildings,
some of which consist of several connected floors. It
is concluded that this method results in plausible floor
plans, over which a designer has control by defining func-
tional constraints. Furthermore, the method is efficient
and easy to implement and integrate into the larger
context of procedural modeling of urban environments.
INTRODUCTION
Game worlds increasingly often feature highly detailed
open worlds. Notable recent examples include Assassin’s
Creed, GTA IV and Oblivion, where players can explore
beautiful cities, of which each building has been modeled
by hand. Manual modeling of these city models involves
an enormous amount of effort, putting a huge burden
on the budget for game development companies. In
particular, this is the reason that, in games, city buildings
either have no interior (they consist only of a fa¸cade)
or a fixed set of interiors is repeated all over the city.
Urban environments are therefore one of many areas
where automated procedural generation methods can
excel, by providing a limitless amount of varied content
for a fraction of the cost.
The procedural generation of a city entails a number of
ingredients, each with its specific procedures and genera-
tion techniques: district topology (city center, suburbia),
road network (ring road, streets), division of open space
into parcels (building lots), building fa¸cades, floor plans
(room layouts) and interior furniture (chairs, tables).
In this paper, we focus on an important if somewhat
neglected ingredient: floor plans. We propose a novel
and flexible technique for generating procedural floor
plans subject to user-defined constraints. Using these
constraints, game designers control both the topological
layout of the building (e.g. which room comes next to
which other room) and the areas of the room. Further-
more, the method guarantees reachability of all rooms
and over multiple floors.
The remainder of this paper is structured as follows.
We first survey previous work on procedural modeling
of urban environments, focusing on floor plan genera-
tion. Then we present our constrained growth method
for procedural floor plans. We show several example
building interiors generated by this technique. And last,
we discuss our method advantages and limitations, and
propose future extensions and potential applications.
RELATED WORK
Procedural modeling techniques have been proposed for
almost every aspect of virtual worlds, ranging from land-
scapes to buildings. An extensive survey of procedural
modeling techniques can be found in Smelik et al. (2009).
Regarding buildings, L-systems, previously applied to
plant models (Prusinkiewicz and Lindenmayer 1990),
were among the first automated techniques used for
building fa¸cades (Parish and M¨uller 2001). In recent
years, more specialized rewriting systems have been pre-
sented for this purpose: the split grammars (Wonka et al.
2003) and shape grammars (M¨uller et al. 2006).
Here, we focus on procedural techniques for building floor
plans, i.e. the creation of a suitable layout of the different
rooms inside a building. Greuter et al. (2003) create a
floor plan as a combination of 2D shapes. However, this
floor plan is only used for extruding building fa¸cades.
Shape grammars, typically applied to building fa¸cades,
can also create floor plans, as shown by Rau-Chaplin
et al. (1996) in their LaHave House project. Their system
1
generates a library of floor plans, each of which can be
customized and automatically transformed into assembly
drawings. It uses shape grammars to create a plan
schema containing basic room units. Possible groupings
of individual units are recognized to define functional
zones like public, private or semi-private spaces. After
generating the required geometric data, such as the room
unit dimensions and the location of walls, a specific
function is assigned to each room. The rooms are filled
with furniture, by fitting predefined layout tiles from an
extensive library of individual room layouts.
Martin (2006) proposes a graph-based method, in which
nodes represent the rooms and edges correspond to con-
nections between rooms (e.g., a door). This graph is
generated by a user-defined grammar. Starting from the
front door, public rooms are added. Each of these public
rooms is assigned a specific function (dining room, living
room, etc.). Subsequently, private rooms are attached to
the public rooms and, finally, stick-on rooms like closets
or pantries are introduced. This graph is transformed to
a spatial layout, by determining a 2D position for each
room. For each node, depending on the desired size of
the room, a specific amount of ”pressure” is applied to
make it expand and fill up the remaining building space.
Hahn et al. (2006) present a method tailored for generat-
ing office buildings. Starting from the building structure,
they first position all elements that span across multiple
floors, e.g. elevator shafts, staircases. Then, the building
is split up into a number of floors. On each of them a
hallway subdivision is applied, adding straight hallway
segments or rectangular loops. Next, the remaining re-
gions are subdivided into rooms, for which geometry is
created and appropriate objects are placed. A notable
feature of this system is that each step is executed just
in time: based on the player’s position, floors and rooms
are generated or discarded. A discarded room can be re-
stored exactly in its previous state, by re-using the same
random seed in the procedure, followed by re-applying
all changes to its objects, caused by the player.
Marson and Musse (2010) introduce a room subdivision
method based on squarified treemaps. Treemaps recur-
sively subdivide an area into smaller areas, depending
on a specific importance criterion. Squarified treemaps
simultaneously try to maintain a length to width ratio
of 1. Their methods input is the basic 2D shape of the
building and a list of rooms, with desired dimensions
and their functionality, e.g. social area, service area and
private area. First these functionality areas are added
to the treemap. These areas are again subdivided to
create each room. Possible connections between room
types are pre-defined; based on this, doors are placed
between the rooms in the generated floor plan. Using
the A* algorithm, a shortest path is determined, visiting
all rooms that need a connection with the corridor. This
path is transformed into a corridor, and all rooms are
adjusted to make room for it.
Tutenel et al. (2009) applied a generic semantic layout
solving approach to floor plan generation. Every type of
room is mapped to a class in a semantic library. For each
of such semantic classes, relationships can be defined. In
this context, constraints typically define room-to-room
adjacency, however, other constraints can be defined as
well, e.g. place the kitchen next to the garden, or the
garage next to the street. For each room to be placed,
a rectangle of minimum size is positioned at a location
where all defined relation constraints hold, and all these
rooms expanded until they touch other rooms.
A general overview of the application of constraint solv-
ing in procedural generation can be found in (Tutenel
et al. 2008). Particularly, several approaches define the
creation of room layouts as a space planning problem.
Charman (1993) gives an overview of constraint solv-
ing techniques that, although not specifically focused
on space planning, can be applied to these problems.
He compares several space planners and discusses the
efficiency of these solvers. Due to many recent improve-
ments on constraint solving techniques, his efficiency
concerns are no longer relevant. However, the discussed
planners are still suitable for layout solving. For instance,
the planner he proposed (Charman 1993) works on the
basis of axis-aligned 2D rectangles with variable position,
orientation and dimension parameters, for which users
can express geometric constraints, possibly combined
with logical and numerical operators.
These constraint solving techniques create rectangular
shapes, subject to dimension and adjacency constraints.
However, they are typically quite complex and can still be
time consuming. Moreover, many of them cannot handle
irregular shapes, e.g. L-shaped or U-shaped rooms, which
incidentally also holds for many of the other techniques
discussed above. This is one of the main drawbacks of all
these approaches. Our growth-based approach generates
more flexible results, and does that faster than other
constraint solving techniques.
FLOOR PLAN GENERATION METHOD
This section discusses a novel method for procedurally
generating floor plans. The problem of generating floor
plans is primarly a problem of generating the appropri-
ate layout for rooms, i.e. their location and area. We
believe that a proper procedural floor plan is one that
follows the same principles (and, as a consequence, the
appearance) of real building architecture. Our method
has drawn its inspiration from real life architectural floor
plans, where geometric grids are used as a canvas for
hand drawing building interiors. Fig. 1 illustrates a real
architectural floor plan example, where a point grid is
visible. Our method uses a grid-based algorithm for
placing and growing rooms within a building layout. We
describe how this algorithm hierarchically creates a floor
plan by generating building zones followed by its room
areas, both of which are constrained by adjacency and
connectivity relationships.
Figure 1: Example of a real architectural floor plan,
using a geometric point grid
Hierarchical layout
The building layout is defined in a hierarchic manner.
Certain types of rooms can be grouped, allowing the
building to be subdivided in zones for these room groups,
followed by subdividing these zones into sub-zones, or
into specific rooms. This approach is similar to the
method of Marson and Musse (2010), which was dis-
cussed above. An example of a simple hierarchic building
layout is shown in Fig. 2. In this example, we subdivide
a house into two zones: a public zone and a private zone.
The public zone is then subdivided into a dining room,
a kitchen and a living room and the private zone into
a hallway, two bedrooms and a bathroom. This entails
that the room generation algorithm is applied twice, once
for each level in the hierarchy. Note that this hierarchy
can of course have more than two levels.
Because of the hierarchic approach, we do not allow
adjacency constraints to be defined between two rooms
from different zones. We can however put adjacency
constraints between a room and a parent zone, e.g. in
the previous example, we can demand that the hallway
from the private zone is adjacent to the public zone to
allow a connection with this public zone through the
hallway.
Figure 2: This is an example of a hierarchic subdivision
of a building. (a) shows a building subdivided into
two areas, (b) shows these areas subdivided into their
required rooms.
Room placement
Our method uses a grid as the basis for the room place-
ment and expansion process. There are two advantages
in using this representation. Firstly, it maps closely to
the way architects design floor plans. Secondly, it is effi-
cient to traverse and expand in a 2D grid, which allows
our floor plan generation method to execute fast. Our
grid representation (a matrix) does not limit a floor to a
rectangular shape. We fit non-rectangular buildings in a
grid that includes space marked as outdoor.
The method starts with a building footprint polygon,
which we rasterize into the 2D grid. Additional input
includes:
the grid cell dimension in meters (e.g. 0.5m);
a list of room areas to generate;
for each room area its corresponding house zone
(public, private, hallway) and its preferred relative
area (or size ratio);
adjacency and connectivity constraints.
The output it produces is a floor plan, consisting in room
areas, interior and exterior walls, and doors.
In the room placement phase, the initial positions for
each room are determined. For this, we create a separate
grid of weights of the same dimensions as the building
grid. Initially, cells inside the building get a weight of
one and cells outside get a weight of zero. Many of the
rooms in a building are preferably placed next to an outer
wall. Therefore, to apply this constraint, the weights
are altered. A straightforward way to do this would be
to set weights of cells not connected to the outside to
zero. However, placing initial positions adjacent to a
wall does not always result in plausible results, as they
tend to cause less regular room shapes. Therefore, we
use a different approach. Based on the size ratio of the
room and the total area of the building, we can estimate
a desired area of the room. Cells positioned at least a
specific distance (based on this estimated area) away
from the walls are assigned a weight of 1. This results
in much more plausible room shapes.
This phase also deals with the defined adjacency con-
straints. The adjacency constraints are always defined
between two rooms, e.g. the bathroom should be next
to the bedroom, the kitchen should be next to the living
room, etc. When selecting the initial position of a room,
we use the adjacency constraints to determine a list of
rooms it should be adjacent to. We check whether these
rooms already have an initial position. If there is, we
alter the weights to high values in the surroundings of
the initial positions of the rooms it should be adjacent to.
This typically results in valid layouts; however there is a
small chance that the algorithm grows another room in
between the rooms that should be adjacent. To handle
this case, we reset the generation process if some of the
adjacency constraints were not met.
Based on these grid weights, one cell is selected to place
a room, and the weights around the selected cell are
set to zero, to avoid several initial positions of different
rooms to be too close to each other.
Room expansion
We use a growth-based method for determining the pre-
cise shape of rooms. Our algorithm gradually grows
rooms from initial room positions until the building inte-
rior is filled. The expansion process is done by appending
adjacent grid cells to rooms.
Algorithm 1 outlines the expansion of rooms in our
method. It starts with a grid
m
containing the initial
positions of each room. It then picks one
room
at a time,
selected from a set of available
rooms
(
SelectRoom
), and
expands the room shape to the maximum rectangular
space available (
GrowRect
). This is done until no more
rectangular expansions are possible. At this point, the
process resets
rooms
to the initial set, but now considers
expansions that lead to L-shaped rooms (
GrowLShape
).
In a final step, the algorithm scans for remaining empty
cells and assigns them to a nearby room (FillGaps).
Algorithm 1: Room expansion algorithm
in : A list of rooms to be placed l
in : A list of room area ratios r
in : A building grid m
out : A floor plan defined in m
rooms BuildRoomSet(l);
while rooms 6=do
room SelectRoom(rooms, r);
canGrow GrowRect(room, m, r[room]);
if ¬canGrow then
rooms \ {room};
end
end
rooms BuildRoomSet(l);
while rooms 6=do
room SelectRoom(rooms, r);
canGrow GrowLShape(room, m);
if ¬canGrow then
rooms \ {room};
end
end
if HasEmptySpaces(m)then
FillGaps(m);
end
In
SelectRoom
the next room to be expanded is chosen
on the basis of the defined size ratios
r
for each room.
The chance for a room to be selected is its ratio relative to
the total sum of ratios, defined in
r
. With this approach,
variation is ensured, but the selection still respects the
desired ratios of room areas.
The first phase of this algorithm is expanding rooms
to rectangular shapes (
GrowRect
). In Fig. 3 we see an
example of the start situation (a) and end (b) of the
rectangular expansion phase for a building where rooms
black, green and red have size ratios of, respectively, 8, 4
and 2. Starting with rectangular expansion ensures two
characteristics of real life floor plans: (i) a higher priority
is given to obtain rectangular areas and (ii) the growth is
done using the maximum space available, in a linear way.
For this, all empty line intervals in the grid
m
to which
the selected
room
can expand to are considered. The
maximum growth, i.e. the longest line interval, which
leads to a rectangular area is picked (randomly, if there
are more than one candidates). A room remains available
for selection until it can not grow more. This happens if
there are no more directions available to grow or, in the
rectangular expansion case, if the room has reached its
maximum size. This condition also prevents starvation
for lower ratio rooms, since size ratios have no relation
with the total building area. In Fig.3 (b), all rooms have
reached their maximum size.
Of course, this first phase does not ensure that all avail-
able space gets assigned to a room. In the second phase,
all rooms are again considered for further expansion,
now allowing for non-rectangular shapes. The maximum
growth line is again selected, in order to maximize ef-
ficient space use, i.e. to avoid narrow L-shaped edges.
In this phase, the maximum size for each room is no
longer considered, since the algorithm attempts to fill all
the remaining empty space. Furthermore we included
mechanisms for preventing U-shaped rooms. Fig. 3 (c)
illustrates the result of the L-shaped growth step on the
previous example. The final phase scans the grid for
remaining empty space; this space is directly assigned
to the room which fills most of the adjacent area.
With the described mechanism, our growth algorithm
generates valid floor plans. Even with all the constraints,
each room layout problem has many valid solutions.
Therefore, there is quite some variation in generated floor
plans. Since our grid-based algorithm is fast enough, we
can collect multiple alternative solutions for each situa-
tion. We execute our growth algorithm N(parametriz-
able) times, obtaining Ndifferent but valid floor plans.
We can either randomly select one of alternatives, or
we can evaluate and rank the solutions (by e.g. prefer-
ence for smaller hallways or the least amount of corners
in room), and pick the best solution. Post-processing
will convert the grid to a geometric representation of
rooms, and ensures the creation of doors and windows
or extrusion of walls.
Room connectivity
Our method ensures connectivity between rooms by pro-
cedurally placing inner doors. This process is more
elaborate than simply making all rooms reachable from
e.g. the entrance of the building. For plausible results,
(a) (b) (c)
Figure 3: Example for the room expansion algorithm: (a) initial room positions (b) rectangular growth (where rooms
reached their maximum size) (c) L-Shape growth.
room connectivity needs to influence and be influenced
by the topology of the building. We achieved this us-
ing connectivity constraints combined with a specialized
connectivity algorithm.
Connectivity constraints, declared as input, state that
two rooms should be directly connected. Stating that
room A should be connected to room B will create a door
that opens from room A to room B. These constraints
influence the room layout, as they are also interpreted
as adjacency constraints. After placement and expan-
sion of rooms, we proceed to a post-processing phase,
in which, among other things, our connectivity algo-
rithm is executed. For this, the grid representation is
no longer appropriate, as it is more natural to consider
inner walls rather than room areas. Therefore, in this
post-processing phase matching inner walls for the rooms
are created first.
The connectivity algorithm places doors in these walls,
influenced by the building topology and connectivity
constraints. It should ensure the common connectivity
principles of a building (e.g. hallway always leads to
adjacent public rooms), while the constraints should only
be used to declare specific cases. Our aim is to emulate
real life architecture in its basic connectivity concepts,
independent of style or type of buildings. Therefore, we
use the notion of private and public rooms to decide on
door placement. The goal is to create full connectivity,
while respecting room privacy.
Our algorithm starts by placing doors between rooms
for which connectivity was explicitly declared. Next,
it connects any hallway to all of its adjacent public
rooms. Unconnected private rooms are then connected,
if possible, to an adjacent public room. Publics rooms
with no connections are connected to an adjacent public
room as well. Finally, our last step is a reachability test.
We examine all rooms and if any is not reachable from
the hallway, we use the adjacency relationships between
rooms to find a path to the unreachable room, and create
the necessary door(s).
With this algorithm, public rooms (and in particular the
hallway) have priority to become focal points, in terms
of connectivity to private rooms. We wanted to bring
about the situation where, for example, two adjacent
private rooms are connected through a common public
room, instead of having a direct door between them. The
hierarchical layout ensures rooms distribution in such a
way that this setup is possible.
The creation of an inner door is implemented in a simple
manner. First, we select a shared wall between the two
rooms to connect, in which the door fits. Next, we
randomly select a segment of that wall and place the
door in its midpoint. Finally, doors are assigned to open
towards the destination room with its hinge towards the
closest corner of the wall.
Hallway
Kitchen
Living room
Bedroom
Bathroom
Figure 4: Example of a relatively simple floor plan in-
cluding four rooms and a hallway.
RESULTS
This section provides examples of floor plans generated
with our method. The first example is a relatively simple
rectangular building (11 x 9 m), for which no hierarchy is
defined, and which should contain four rooms (a bedroom,
a bathroom, a kitchen and a living room) and a hallway.
We defined a connectivity constraint between the kitchen
and the living room. An example of a generated floor
plan for this building is shown in Fig. 4. Notice that the
connectivity constraint is satisfied, and that all rooms
are reachable. Generating these kind of floor plans takes
less than 100 ms.
Hallway
Bedroom 2
Bedroom 3
Bathroom 2
Great room
Kitchen and
dining room
Master bedroom
Master bathroom
Closet
Hallway
Bedroom 2 Bedroom 3
Bathroom 2
Great room
Kitchen and dining room
Master bedroom
Master bathroom
Closet
Figure 5: Two examples of a more complex floor plan, built up of a public zone with a great room and kitchen and
dining room, a private zone with a master bedroom and walk-in closet, master bathroom and a private zone with a
hallway, a bathroom and two bedrooms.
In our second example, we show more complex floor
plans, inspired from real floor plans of North-American
style villas. The building (which is L-shaped, measuring
17 x 20 m with a top part of 7 x 9.5m and a bottom part
of 17 x 10.5 m) is subdivided into three areas: one public
and two private zones. There are adjacency constraints
defined between the public area and the two private
areas. The public area includes a great room and a room
for a kitchen and a dining area. These rooms have a
connectivity constraint defined. The first private area
includes a master bedroom with an adjacency constraint
to the public area, a master bathroom and a walk-in
closet both connected to the master bedroom. The
second private area includes a hallway with an adjacency
constraint to the public area and three other rooms (a
bathroom and two additional bedrooms), all connected
to this hallway. Two floor plan examples for this building
are shown in Fig. 5.
One clearly notices L-shaped areas not only for the indi-
vidual rooms but also for the different areas within the
floor plan. This creates more varied and less restrictive
results than methods that are limited to rectangular
rooms. Because of the increased complexity these floor
plans take longer to generate: on average around one
second per floor plan.
In our method, we included a simple mechanism for
placing exterior elements, i.e. outer doors and windows.
These wall elements are declared as requirements for
each room. They are placed using the same method as
for inner doors. The results show that these elements
are placed correctly, if there is space available. In the
future, it would be interesting to consider generating
these elements automatically.
Multiple floors are also supported by our method. When
generating the ground floor, a staircase (or elevator)
room is generated. In the subsequent floors, this room
is duplicated, fixed to the same position as the floor be-
low. Adjacency and connectivity constraints for staircase
rooms still hold as in normal rooms. Our results show
that room expansion in higher floors is not affected by
the initial duplication of the staircase. Rooms expand
naturally around the staircase, respecting all constraints.
An example of a two floor house is shown in Fig. 6.
Discussion
We discussed how our floor plan generation method
is suitable for procedural building generation, since it
provides fast and plausible floor plans. However, there
is some room for improvement. Firstly, our connectivity
algorithm identifies between which two rooms a door
needs to be placed, but the actual location of the door
is at random within the selected wall segment. This
sometimes creates situations where the walk paths inside
the building are not optimal. A smart approach for
positioning these doors would be a helpful extension.
Another potential issue arises from the fact that we
currently can not constrain the dimensions of a specific
room. Our approach often creates L-shaped rooms, which
might not be desirable in some particular cases. An
example of this is a garage, which needs to have specific
dimensions such that a car can be efficiently parked. An
L-shaped garage makes not much sense in that respect.
Constraints on the width-to-height ratio of a particular
room could improve the methods ability for handling
these cases.
Upstairs
hallway
Bedroom 1
Study
Bedroom 2
Bath
room
Entrance
Toilet Staircase Living room
Kitchen
Staircase
Figure 6: An example floorplan of a two floor house. Downstairs (left of the figure) an entrance, connected to a toilet,
a staircase and a kitchen and living room, upstairs, the staircase is repeated and is connected to a hallway which in
turn is connected to two bedrooms (one of which is connected with a study) and a bathroom.
Since we use a grid-based algorithm, buildings with ar-
bitrary angles are not supported. However, there are
straightforward extensions for such cases as well. As in
the technique used by Greuter et al. (2003), a building’s
outer shape can be created by combining multiple ge-
ometric shapes. If all of these basic shapes would be
constrained to axis-aligned polygons (with an arbitrary
rotation in reference to the buildings orientation), these
parts could be hierarchically subdivided into separate
areas with our approach, and later combined into one
complete building.
CONCLUSIONS
We can conclude that the method presented here effi-
ciently generates valid and plausible building floor plans.
We validate it in two ways. First, we visually compared
floor plans generated by our method with many real-
world plans of North-American houses, with which they
typically matched well. However, although the method
is very suitable for generating floor plans of houses, for
highly regular structured spaces, such as some types
of office buildings, more simple and specialized subdi-
vision methods would be more efficient and effective.
Second, we have consulted with architects and other
experts on the output generated, and received valuable
feedback. This has already resulted in further tuning of
the method’s parameters and constraints, for example
on plausible adjacencies and desirable floor topologies.
In the near future, we also expect this feedback to lead
to the introduction of additional consistency tests.
An area which we could improve upon is the scoring
mechanism for rating the set of alternative layouts gener-
ated by the method. This score could be a combination
of the adjacency constraint satisfaction with a number
of heuristics, e.g. minimum amount of internal doors,
minimum amount of internal wall segments, etc.
Our implementation of the method performs well, due
to its low complexity and efficient data structures. Al-
though it is by far not optimized, it is still suitable in the
context of dynamic generation of virtual worlds, where,
e.g., the interior of procedural buildings is generated
within a small frustum around the player.
As future work, we would like to embed this method in
the broader context of generating fully featured procedu-
ral cities. For this, we would have, among other things,
to integrate the method with a fa¸cade generation sys-
tem, resulting in complete buildings. Also, by using our
semantic layout solving approach (Tutenel et al. 2009),
we could fill the procedural interiors with appropriate
furniture.
In our view, the constrained growth floor plan generation
method is a small but significant step towards the goal
of realistic and full-featured procedural cities.
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ACKNOWLEDGEMENTS
This work was supported in part by the Portuguese
Foundation for Science and Technology under grant
SFRH/BD/62463/2009. This work has also been sup-
ported by the GATE project, funded by the Netherlands
Organization for Scientific Research (NWO) and the
Netherlands ICT Research and Innovation Authority
(ICT Regie).
BIOGRAPHY
Ricardo Lopes
is a PhD student from the Delft Univer-
sity of Technology. His current research interests include:
adaptivity in games, player modeling, interpretation
mechanisms for in-game data and (on-line) procedural
generation techniques. Ricardo got his master’s degree
in Information Systems and Computer Engineering at
the Technical University of Lisbon, in Portugal.
Tim Tutenel
is a PhD candidate from Delft Univer-
sity of Technology, participating in a research pro ject
entitled ”Automatic creation of virtual worlds”. His
research focus is on layout solving, object semantics and
object interactions. In particular, he is researching how
semantics can improve procedural generation techniques.
Tim got his master’s degree in Computer Science at the
Hasselt University in Belgium.
Ruben Smelik
is a scientist at the TNO research insti-
tute in The Netherlands. He is a PhD candidate on a
project entitled ”Automatic creation of virtual worlds”,
in close cooperation with Delft University of Technology.
This project aims at developing new tools and techniques
for creating geo-typical virtual worlds for serious games
and simulations. Ruben holds a masters degree in com-
puter science from the University of Twente.
Klaas Jan de Kraker
is a member of the scientific
staff at the TNO research institute. He holds a PhD
in computer science from Delft University of Technol-
ogy. He has a background in computer-aided design
and manufacturing, collaboration applications, software
engineering (methodologies), meta-modeling and data
modeling. Currently he is leading various simulation
projects in the areas of simulation based performance as-
sessment, collective mission simulation, multifunctional
simulation and serious gaming.
Rafael Bidarra
is associate professor Game Technology
at the Faculty of Electrical Engineering, Mathematics
and Computer Science of Delft University of Technology,
The Netherlands. He graduated in electronics engineer-
ing at the University of Coimbra, Portugal, and received
his PhD in computer science from Delft University of
Technology. He leads the research line on game tech-
nology at the Computer Graphics Group. His current
research interests include: procedural and semantic mod-
eling techniques for the specification and generation of
both virtual worlds and game play; serious gaming; se-
mantics of navigation; and interpretation mechanisms
for in-game data. He has published many papers in inter-
national journals, books and conference proceedings, and
has served as member of several program committees.
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1 Graphical modeling using L-systems.- 1.1 Rewriting systems.- 1.2 DOL-systems.- 1.3 Turtle interpretation of strings.- 1.4 Synthesis of DOL-systems.- 1.4.1 Edge rewriting.- 1.4.2 Node rewriting.- 1.4.3 Relationship between edge and node rewriting.- 1.5 Modeling in three dimensions.- 1.6 Branching structures.- 1.6.1 Axial trees.- 1.6.2 Tree OL-systems.- 1.6.3 Bracketed OL-systems.- 1.7 Stochastic L-systems.- 1.8 Context-sensitive L-systems.- 1.9 Growth functions.- 1.10 Parametric L-systems.- 1.10.1 Parametric OL-systems.- 1.10.2 Parametric 2L-systems.- 1.10.3 Turtle interpretation of parametric words.- 2 Modeling of trees.- 3 Developmental models of herbaceous plants.- 3.1 Levels of model specification.- 3.1.1 Partial L-systems.- 3.1.2 Control mechanisms in plants.- 3.1.3 Complete models.- 3.2 Branching patterns.- 3.3 Models of inflorescences.- 3.3.1 Monopodial inflorescences.- 3.3.2 Sympodial inflorescences.- 3.3.3 Polypodial inflorescences.- 3.3.4 Modified racemes.- 4 Phyllotaxis.- 4.1 The planar model.- 4.2 The cylindrical model.- 5 Models of plant organs.- 5.1 Predefined surfaces.- 5.2 Developmental surface models.- 5.3 Models of compound leaves.- 6 Animation of plant development.- 6.1 Timed DOL-systems.- 6.2 Selection of growth functions.- 6.2.1 Development of nonbranching filaments.- 6.2.2 Development of branching structures.- 7 Modeling of cellular layers.- 7.1 Map L-systems.- 7.2 Graphical interpretation of maps.- 7.3 Microsorium linguaeforme.- 7.4 Dryopteris thelypteris.- 7.5 Modeling spherical cell layers.- 7.6 Modeling 3D cellular structures.- 8 Fractal properties of plants.- 8.1 Symmetry and self-similarity.- 8.2 Plant models and iterated function systems.- Epilogue.- Appendix A Software environment for plant modeling.- A.1 A virtual laboratory in botany.- A.2 List of laboratory programs.- Appendix B About the figures.- Turtle interpretation of symbols.