Interactive Rendering and Stylization of Transportation Networks

using Distance Fields

Matthias Trapp, Amir Semmo and J

urgen D

ollner

Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany

Keywords:

Transportation Networks, 3D Visualization, Image-based Rendering, Distance Fields, Shading, Map Design.

Abstract:

Transportation networks, such as streets, railroads or metro systems, constitute primary elements in cartog-

raphy for reckoning and navigation. In recent years, they have become an increasingly important part of 3D

virtual environments for the interactive analysis and communication of complex hierarchical information, for

example in routing, logistics optimization, and disaster management. A variety of rendering techniques have

been proposed that deal with integrating transportation networks within these environments, but have so far

neglected the many challenges of an interactive design process to adapt their spatial and thematic granularity

(i.e., level-of-detail and level-of-abstraction) according to a user’s context. This paper presents an efﬁcient

real-time rendering technique for the view-dependent rendering of geometrically complex transportation net-

works within 3D virtual environments. Our technique is based on distance ﬁelds using deferred texturing

that shifts the design process to the shading stage for real-time stylization. We demonstrate and discuss our

approach by means of street networks using cartographic design principles for context-aware stylization, in-

cluding view-dependent scaling for clutter reduction, contour-lining to provide ﬁgure-ground, handling of

street crossings via shading-based blending, and task-dependent colorization. Finally, we present potential

usage scenarios and applications together with a performance evaluation of our implementation.

1 INTRODUCTION

The efﬁcient rendering and visualization of trans-

portation networks within interactive virtual 3D en-

vironments is an important feature for a number of

today’s applications, such as Google Maps and Earth

or Bing Maps. The presented work enables an ef-

ﬁcient way of rendering complex transportation net-

works with a ﬂexible parametrization and stylization.

Motivation. Transportation networks represent im-

portant features in 3D geovirtual environments, such

as virtual city and landscape models. This class of in-

frastructure networks comprise, e.g., street networks,

rail road networks, and cycling tracks. Their high-

quality visualization is crucial for a number of ap-

plications within these environments, such as navi-

gation systems and digital maps to support orienta-

tion and wayﬁnding in the real world. In addition

to high visual contrasts (Vaaraniemi et al., 2011), the

visualization quality comprises a number of further

aspects such as anti-aliasing, and coherence in the

continuation of line segments. To achieve this for

the stated applications, dynamic scaling (Kersting and

ollner, 2002) or zoom-dependent rendering of mas-

sive transportation networks using abstract or carto-

graphic styles is required. One can basically distin-

guish between three major approaches how current

systems and applications perform their rendering:

Geometry-based. This approach relies on explicitly

pre-computed textured geometry for network seg-

ments and junctions, based on the network topol-

ogy. Depending on the level-of-details required,

this exhibits memory consumptions of the result-

ing geometries and textures.

Texture-based. This approach rasterizes the map-

ping results of geometry-based approaches into

a single or multiple textures required for textur-

ing the underlying terrain model (Kersting and

ollner, 2002). Using this approach, however,

the rendering quality can suffer due to sampling

and aliasing artifacts caused by insufﬁcient tex-

ture resolution.

Stencil-based. This approach uses the concept of

shadow volumes for rendering transportation net-

works on top of digital terrain models (Vaaraniemi

et al., 2011). It extrudes the network geometry

207

Trapp M., Semmo A. and Döllner J..

Interactive Rendering and Stylization of Transportation Networks using Distance Fields.

DOI: 10.5220/0005310502070219

In Proceedings of the 10th International Conference on Computer Graphics Theory and Applications (GRAPP-2015), pages 207-219

ISBN: 978-989-758-087-1

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

(a) Perspective projection at a high zoom level. (b) High contrast orthographic projection.

Figure 1: Rendering of an OpenStreetMap data set using different map stylizations.

and applies a stencil shadow volume algorithm.

The shadow volume must be recomputed if the

network conﬁguration changes, e.g., due to inter-

active ﬁltering.

These classes of existing approaches for rendering

and visualization of transportation networks often as-

sume a static input geometry, mapped to textured ge-

ometry for rendering, or additionally require the com-

putation of intermediate geometry (shadow volumes).

However, there are visualization approaches that re-

quire the handling of dynamic networks as the result

of ﬁltering or modiﬁcation during the visualization

process (Haunert and Sering, 2011). Further, an in-

creasing geometric complexity of transportation net-

works due to more detailed representations also in-

creases the memory footprint of each of these tech-

niques accordingly. Furthermore, the change in net-

work colorization or similar tasks requires an update

of the intermediate rendering representations, which

can become a time consuming process, depending on

the complexity of a transportation network.

Problem Statement. The above characteristics of

existing techniques limits their application with re-

spect to view-dependent rendering of massive, possi-

bly dynamic transportation networks that supports in-

teractive ﬁltering and colorization by simultaneously

retaining a low memory footprint. Based on these

functionalities, the design of an interactive rendering

technique is faced with the following challenges and

requirements:

R1. Pre-processing of discrete level-of-details con-

sume additional memory and often yield incoher-

ent rendering when switching between these lev-

els during zooming or within perspective projec-

tion. Therefore, levels of detail should be com-

puted on-the-ﬂy during the rendering and based

on current viewing settings.

R2:. Increasingly detailed transportation networks

require high amounts of main and/or video mem-

ory. Therefore, the network representation should

exhibit a minimal memory footprint and facilitate

fast updates.

R3. View-dependent cartographic stylization of

transportation networks are key features for a

number of applications. Therefore, the rendering

technique should provide a sufﬁcient parametriza-

tion, i.e., covering level-of-detail rendering, as

well as interactive ﬁltering and highlighting.

Contributions. With respect to the challenges

stated above, this paper presents a new real-time

rendering technique for the cartographic rendering

and visualization of complex transportation networks.

It is based on a single-pass computation of dis-

tance ﬁelds for an effective geometric representation

(Frisken et al., 2000) combined with parameterized

stylizations performed in screen space. The presented

approach relies on a compact memory representation

of transportation networks to provide a minimal mem-

ory footprint compared to existing techniques. Based

on this representation, route geometry is efﬁciently

generated on-the-ﬂy during rendering, enabling inter-

active modiﬁcations of the depicted contents. Further,

the technique does not rely on adjacency information.

The presented stylization model effectively de-

couples geometry from appearance parameters such

as width, color, or texture. It facilitates interactive

level-of-detail (LoD) as well as level-of-abstraction

(LoA) rendering (Semmo et al., 2012). It further

supports localization of route networks (e.g., color

schemes, line styles), and interactive view-dependent

ﬁltering based on virtual lens metaphors (Tominski

et al., 2014). To summarize, this paper makes the fol-

lowing contributions to the challenges stated above:

1. It presents a concept for high-quality cartographic

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208

rendering, which is exempliﬁed for complex street

networks.

2. It provides an interactive hardware-accelerated

rendering technique that facilitates a minimal

memory footprint for network representation.

3. It introduces interactive stylization and coloriza-

tion mechanisms using deferred texturing and dis-

tance transforms.

The presented approach has a number of applications

beyond the rendering and visualization of street net-

works. For instance, it can be applied to the rendering

and stylization of planar graphs as well as visualiza-

tion of aircraft trajectories.

The remainder of this paper is structured as fol-

lows. Section 2 reviews related work concerning

the visualization and rendering of transportation net-

works, in particular considering street networks. Sec-

tion 3 discusses design principles of transportation

network depictions from the viewpoint of cartogra-

phy. Section 4 introduces the concept for rendering

image-based rendering of transportation networks us-

ing distance ﬁelds and deferred texturing. Section 5

gives details regarding a fully hardware-accelerated

implementation of this concept. Further, Section 6

demonstrates the rendering technique using different

application examples and discusses limitations and

ideas for future work. Finally, Section 7 concludes

this paper.

2 RELATED WORK

Transportation networks are well-researched in 2D

map design (MacEachren, 1995; Kraak and Ormel-

ing, 2003; Tyner, 2010), but only few works deal

with their representation in interactive 3D virtual en-

vironments. In the following, we give an overview

on related works that deal with the modeling and ren-

dering of transportation networks including position-

ing in 3D space and visualization techniques that deal

with the challenges of visual clutter and occlusion.

2.1 Modeling and Rendering of

Transportation Networks

Generalization is a key concept for modeling and

transforming transportation networks into human-

readable maps (MacEachren, 1995; Jiang and Clara-

munt, 2004), comprising operators such as sim-

pliﬁcation, displacement, and deformation (Foerster

et al., 2007), while preserving spatial relationships

and topology (Tversky and Lee, 1999; Agrawala and

Stolte, 2001). Generalization techniques have been

effectively used for the design of destination maps

(Kopf et al., 2010) and sketch maps to draw contour

lines excessively wavy or fuzzy and express uncer-

tainty (Tversky and Lee, 1999; Skubic et al., 2004).

Most generalized models base on connectivity graphs

where vertices represent segments of named street

and links represent street intersections (Jiang and

Claramunt, 2004). These models are typically pro-

vided via commercial products (e.g., Navteq), gen-

erated procedurally (Galin et al., 2010; Bene

s et al.,

2014), or authored in hierarchies (Galin et al., 2011).

In our work, we use OpenStreetMap as a collaborative

platform with free access to geospatial data (Haklay

and Weber, 2008).

A common challenge in rendering transportation

networks in 3D virtual environments is the projec-

tion onto digital terrain models. First approaches

use geometry-based methods to directly combine vec-

tor data with a 3D terrain mesh (Polis et al., 1995;

Weber and Benner, 2001), but only provide pre-

computations without the capability for dynamic styl-

ization. A ﬁrst approach for level-of-detail render-

ing uses texture-based mapping to project vector fea-

tures on 3D terrain models (Kersting and D

ollner,

2002). Similar approaches use principles of shadow

mapping for perspective parameterizations by tak-

ing the current point of view into account (Schnei-

der et al., 2005), continuous level-of-detail methods

(Wartell et al., 2003), and shading for dynamic fea-

ture editing (Bruneton and Neyret, 2008). However,

the approaches are less suited for 3D presentations

of transportation networks in close view distances

because of the limitation in detail and sharpness.

Further, they require explicit level-of-detail mecha-

nisms which results in additional computational costs.

Other methods utilize the hardware-accelerated sten-

cil buffer with a type of shadow volume (Schneider

and Klein, 2007; Vaaraniemi et al., 2011), or com-

pletely rely on screen-space rendering (Ohlarik and

Cozzi, 2011) to reduce computational costs. How-

ever, the later approach is limited with respect to the

generation of view-dependent level-of-abstraction vi-

sualization (Semmo et al., 2012).

Our work presents a compromise between texture-

based and geometric approaches using distance ﬁelds

to effectively reconstruct geometric properties of

complex shapes during shading (Frisken et al., 2000).

We employ distance maps to stylize transportation

networks in real-time utilizing bilinear sampling for

a piecewise-linear approximation of feature contours.

In particular, this approach has been proven effective

for the magniﬁcation of glyph contours, even with

low-resolution distance maps (Green, 2007). Pre-

vious algorithms use vector propagation to compute

InteractiveRenderingandStylizationofTransportationNetworksusingDistanceFields

209

these maps by an approximate Euclidean distance

transform (Danielsson, 1980), e.g., jump-ﬂooding

(Rong and Tan, 2006), or provide work-load efﬁcient

methods to compute an exact distance transform on

the GPU (Cao et al., 2010).

Previous work demonstrates the effective use of dis-

tance ﬁelds for real-time rendering of water surfaces,

where respective distance and orientation informa-

tion is computed to guide the stylization and annota-

tion (Semmo et al., 2013). Our approach works simi-

lar, but exhibits a more straight forward approach for

generating the distance maps because of lines being

a more simpler geometric representation than poly-

gons. In particular, we demonstrate the effective us-

age of distance maps for stylization, such as render-

ing of contour lines, the view-dependent scaling, and

handling of street crossings (Vaaraniemi et al., 2011).

2.2 Visualization Techniques For

Transportation Networks

According to a user’s background, task, and per-

spective view, often too much irrelevant (cluttered)

or too few information is visualized (Shneider-

man, 1996), and thus not a meaningful map de-

sign is provided when rendering transportation net-

works (MacEachren, 1995). To address this con-

cern, major related work is found in focus+context

and zooming-based visualization techniques.

Focus+context describes the concept to visually

distinguish between important or relevant informa-

tion from closely related information (Furnas, 1986).

Many interface schemes exist to allow users to attain

both focused and contextual views of their informa-

tion spaces, i.e., detail+overview, zooming, and cue

techniques (Cockburn et al., 2009). Focus+context

route zooming uses principles of scaling to mag-

nify regions of interest and for disocclusion manage-

ment (Qu et al., 2009). Other techniques employ

global deformations and degressive projections in

panoramic maps for disocclusion of routes (Takahashi

et al., 2006; Falk et al., 2007; Degener and Klein,

2009), or scale surrounding 3D objects (e.g., build-

ings) using view-dependent optimization techniques

(Hirono et al., 2013). Further techniques employ in-

teractive lenses as established means to facilitate the

exploration of transportation networks, e.g., for mag-

niﬁcation (Karnick et al., 2010; Haunert and Sering,

2011), which are quite versatile in their parametriza-

tion for clutter reduction (Tominski et al., 2014).

Another typical approach to deal with the prob-

lem of overcluttered displays is contextual zooming,

where hierarchical route maps of varying resolution

may be used for effective navigation (Wang et al.,

Figure 2: Exemplary maps depicting transportation net-

works: a) Paris in 1787 designed by Louis Brion de la Tour,

b) the famous Pocket Underground map from 1933 by Harry

Beck, c) London in 1913, d) contemporary map of Paris.

2014). A ﬁrst approach for dealing with the prob-

lem of visual clutter in 3D perspective views has

been presented in (Vaaraniemi et al., 2011) via view-

dependent scaling of stylization features. The con-

cept presented in our work supports the required

parametrization for these visualization techniques.

3 DESIGN PRINCIPLES FROM

CARTOGRAPHY

Well-designed illustrations of transportation net-

works accentuate the ﬁgure-ground relation-

ship, and communicate hierarchical and meta-

information (e.g., street names). To this end, we

studied textbooks on map design and thematic

cartography (Imhof, 1975; MacEachren, 1995; Kraak

and Ormeling, 2003; Tyner, 2010), and examined the

works by famous cartographers and map designers

(e.g., Harry Beck, Figure 2b). From our empirical

analysis, we extracted three groups of design aspects

according to the 2D semiotic model (Bertin, 1981):

graphical elements (e.g., lines, points) and their

position, and graphical variables (e.g., color, line

thickness, decoration elements such as labels).

Graphical Elements. In general, two approaches

for the depiction of transportation networks exist:

(1) non-explicit elements by the principle of sur-

roundedness (MacEachren, 1995) for effective ﬁgure-

ground destinction on maps, for instance where

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210

Figure 3: Schematic overview of the rendering pipeline for transportation networks. Given a compact network representa-

tion and stylization parameters, textured geometry and distance ﬁelds are synthesized per network category within a single

rendering pass. The results are then used for stylization and image composition in a post-processing pass.

streets are formed as a unit via enclosing fea-

tures (Figure 2a), and (2) the explicit graphical de-

piction via connected lines and points (nodes) (Fig-

ure 2b-d) on which we focus in this work. In mod-

ern maps, (P1) contour lines often surround ﬁne-

textured ﬁlls or solid colors to add visual contrast and

improve the ﬁgure-ground perception (MacEachren,

1995). Hierarchical representations of street networks

often have (P2) primary streets overlap secondary or

tertiary streets. In sketch maps, these lines or con-

tours are often drawn excessively wavy or fuzzy to

express uncertainty. Following a level-of-abstraction

concept, (P3) dynamic ﬁltering and scaling of these

geometric features improves the perception of roads

at high view distances and avoids overcluttering. The

choice of shape often varies in thematic cartogra-

phy, ranging from solid lines to dotted representations

(e.g., to discern between car driving and biking di-

rections). Finally, labels are primary design elements

to enrich networks with meta-information. By con-

vention, (P4) names follow principal line directions

and are placed within streets, or outside line segments

and oriented with links, e.g., the latter in schematized

maps (Figure 2b), to ensure legibility (Imhof, 1975).

Graphical Variables. In many maps, (P5) a hierar-

chy of emphasis is drawn among reference elements,

such as different line weights and colors to portray

different grades of roads (Figure 2b/d). In modern

maps, (P6) streets are tinted using qualitative color

schemes to represent street classes and distinguish

them from the underlying terrain. This association

may enable cognitive grouping of each network type

(Kraak and Ormeling, 2003). To date, standardized

color schemes for transportation networks have not

been established but vary from country to country.

But it can be observed that (P7) yellow established

as a conventional color tone for main streets, with a

discrete gradation towards grey and white shading for

tertiary roads (Figure 2d). Lately, principles for color

blindness have also been examined by the example

of OpenStreetMap (Kr

oger et al., 2013). All these

graphical variables may additionally change accord-

ing to the zoom level to avoid overcluttered displays.

In the following, we consider these principles for an

interactive design process in 3D virtual environments.

4 CONCEPTUAL OVERVIEW

This section presents an overview of the concepts for

our rendering and stylization approaches. Figure 3

shows a schematic overview of our proposed render-

ing pipeline comprising control and data ﬂow. It basi-

cally consists of the following three stages:

Preprocessing. This stage loads and transforms a

given transportation network with its associated

meta data into a compact representation (at-

tributed point cloud) for efﬁcient rendering and a

low memory footprint. This operation is required

to be performed only once per data set (Sec. 4.1).

Distance Transform. Starting from the pre-

processed input, this stage synthesizes textured

polygons of respective widths that are subse-

quently rasterized into distinct distance-ﬁeld

buffers for each network category (Sec. 4.2).

Stylization and Compositing. In this stage, de-

ferred texturing based on the generated distance

ﬁelds is performed in screen space using a sin-

gle post processing pass (Sec. 4.3). It enables

application-wise procedural and raster-based tex-

turing for colorization with level-of-detail sup-

port. The resulting colors of each network cate-

gory are subsequently composited in a bottom-up

approach with respect to their ranking.

The remainder of this section describes the data rep-

resentation and assumptions, followed by a more de-

tailed description of these stages. The data represen-

tation is exempliﬁed for street networks.

InteractiveRenderingandStylizationofTransportationNetworksusingDistanceFields

211

(a) Lines. (b) Polygons. (c) Distances. (d) Styled.

Figure 4: Overview of the processing stages, starting with

input data (a) over generated polygons (b) to distance ﬁelds

4.1 Representation of Networks

One advantage of our approach is that the geometry

of transportation networks can be generated on-the-

ﬂy (R1). This way, the video memory footprint of the

transportation network can be minimized (R2) while

only geometry within the current view-frustum is gen-

erated. We use OpenStreetMap (OSM) data as input

(Haklay and Weber, 2008). OSM provides free access

to a variety of data types for route map synthesis. This

includes not only road data and 2D terrain informa-

tion, but also 2D building footprints, water surfaces,

and speciﬁc landmark information.

According to the design principles of Section 3, car-

tographic route depictions can be synthesized by a

hardware-accelerated rasterization of line segments

(represented by rectangular geometry). Using mod-

ern GPU capabilities (geometry shaders), the geome-

try synthesis and tessellation can be completely per-

formed on graphics hardware, which however re-

quires the geometry of input lines and additional route

data to be represented as per-vertex attributes. To

this end, the attributed topological representation of

a street network is prepared for GPU-based represen-

tation (Sec. 5.1) during a pre-processing stage. The

attributed graph is encoded by a node buffer and a seg-

ment buffer, both suitable for hardware-accelerated

rendering. Therefore, the nodes basically comprises

their position and grade, i.e., the number of segments

adjacent to the respective node, while the segments

comprise indices to the nodes and the rank of a route

segment. In addition to the ranks supported by OSM,

we introduce a speciﬁc rank for highlighted routes

that overrides all OSM ranks. Further, the segments’

lengths in world space units are encoded for optional

length-parametrizations.

4.2 Distance Field Computation

Figure 4 shows the process of computing distance

ﬁelds for a given network conﬁguration. It basi-

cally comprises two stages that can be efﬁciently per-

formed on graphics hardware. Thereby, two basic as-

pects can be distinguished: (1) the primitive conver-

sion between point and polygons and (2) the texturing

of the synthesized polygons.

Geometry Synthesis. The step for geometry syn-

thesis converts point primitives – issued for render-

ing – to triangle strips, similar to (Trapp et al., 2013).

This can be efﬁciently implemented using geometry

shaders to minimize the memory footprint for rep-

resenting a street network. It also enables batching

(Wloka, 2005) to reduce the number of draw calls.

Based on this compact network representation, poly-

gons are created for each line segment’s (Fig. 4(a))

respective cap and segment (Fig. 4(b)). Figure 5

illustrates this process in more detail. Further, ver-

tex texture coordinates are computed for each poly-

gon and yields distance drop-offs during rasterization

(Fig. 4(c)). The resulting polygons can be efﬁciently

encoded using triangle strips.

Figure 5: Schematic overview of the geometry synthesis.

During geometry synthesis, only segments within the

current view-frustum are considered for processing,

i.e., to reduce the amount of geometry information

submitted to the clipping and rasterization stages of

the hardware-accelerated rendering pipeline. The ma-

jor advantage of this concept is the respective compu-

tation of distance ﬁelds per street category. This en-

ables a ﬂexible stylization and image compositing at

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212

the rendering stage.

Distance Texturing. Once the geometry synthesis

has been performed, the respective texture coordi-

nates are interpolated during rasterization. The re-

quired distances are computed using regular bilinear

sampling using fragment shaders. The required dis-

tance textures are effectively represented using a sin-

gle 2D texture array, whose layers can be indexed ac-

cording to the type of geometry (cap or segment) and

the grade of the node. The individual 2D texture layer

encode the distance drop-off. Different distance tex-

tures can be used for caps of different grades, i.e., at

junctions or endings. Using textures for the distance

representation enables ﬂexibility in design and allows

a simpliﬁed implementation.

Blending. One major challenge in street rendering

is to provide seamless transitions between adjacent

and consecutive segments. In (Vaaraniemi et al.,

2011) three different techniques are described for

geometry-based approaches to avoid gaps. Our ap-

proach bypasses this geometric computation prob-

lem by combining segments using minimum blending

(Bavoil and Myers, 2008): given two distance values

src

and d

dst

of fragments belonging to different seg-

ments, the resulting value that is written to the render

buffer is computed by d

res

= min(d

src

, d

dst

4.3 Stylization of Street Networks

Given the generated distance ﬁelds, the stylization is

performed in a single post-processing stage via de-

ferred texturing using individual style parameters de-

ﬁned per network category.

Style Parametrization. Our approach enables the

stylization of segments based on their category. A

style parameter set S = (w, d

, d

) ∈ S basi-

cally comprises the following parameters: the width

w ∈ R

of a geometric segment in world space coor-

dinates; the respective distances d

, d

∈ [0, 1], with

= 1 for differentiation between main and bor-

der segment color (P1); as well as the color of main

and border segments (C

, C

∈ [0, 1]

). In addition, a

style parameter set may comprise raster-based or pro-

cedural 2D texture maps for example-based rendering

(e.g., sketch maps (Kopf et al., 2010)).

Level-of-Detail Concept. The presented style para-

metrization can be further extended to deﬁne level-

of-detail (LoD) variants for each style (R3). This is

especially useful for a number of applications (Sec.

6.1), such as (1) counterbalancing perspective fore-

shortening of segments at high distances from the vir-

tual camera, (2) enable interactive ﬁltering using lens-

based interaction metaphors (Tominski et al., 2014),

as well as (3) the reduction of visual clutter (Jobst and

ollner, 2008). Therefore, the deﬁnition of a style pa-

rameter set is extended with an additional LoD param-

eter lod ∈ [0, 1] ⊂ R to yield a list of LoD tupels S

lod

(lod

, S

), . . . , (lod

, S

)) with lod

< lod

i+1

and S

∈

S . During runtime, a LoD value is computed per ver-

tex and per fragment (Sec. 5.3). Given a value lod, the

neighboring LoD tuple with lod

< lod ≤ lod

i+1

are

fetched and the resulting (interpolated) style is used

lod

= interpolate(S

, S

i+1

, α), with α =

lod−lod

lod

−lod

i+1

(a) (b) (c)

Figure 6: Examples of different street stylizations computed

via a single set of distance ﬁelds.

Style Evaluation. The process of converting the

generated distance ﬁelds into respective colors using

the previously described style deﬁnitions is denoted

as style evaluation. It is performed on a per-fragment

basis using an additional post-processing step.

To outline potential use cases, Figure 6 shows differ-

ent street stylizations computed from a single set of

distance ﬁelds: Figure 6(a) uses a constant line width

for a single stylization parameter set and all street cat-

egories, while Figure 6(b) applies different styliza-

tions for each street category. Figure 6(b) depicts the

highlighting of a single street category that contrasts

the remaining scene.

5 INTERACTIVE RENDERING

This section presents a prototypical implementation

of our concept using OpenGL and the OpenGL Shad-

ing Language (GLSL). The real-time image synthe-

sis is separated into two rendering passes: the ﬁrst

InteractiveRenderingandStylizationofTransportationNetworksusingDistanceFields

213

pass creates the distance ﬁelds using off-screen ren-

dering and the second pass applies stylization using

deferred texturing based on these distance ﬁelds. We

ﬁrst cover the required data structures for hardware-

accelerated rendering, before presenting details for

these two passes.

5.1 GPU-based Data Representation

This section discusses the used GPU data structures

that enable efﬁcient data access and updates, and thus

facilitates efﬁcient rendering, while simultaneously

exhibiting a small memory footprint for the network

representation.

Network Representation. For a compact represen-

tation and efﬁcient access to a network topology,

nodes and segments are stored using individual buffer

objects (Figure 7). A node buffer stores the positions

of a network graph’s vertices. An additional segment

buffer encodes two indices to this buffer and the street

category of the respective segment. This representa-

tion can be considered as attributed point cloud, sim-

ilar to the concept described in (Trapp et al., 2013).

During runtime, the node buffer is bound as vertex-

attribute source, while the segment buffer is drawn

using point primitives. Thus, only one draw call is

required to issue the rendering of a transportation net-

work. Using the indices encoded in each point, a

geometry shader fetches the node positions from the

node buffer as well as the stylization parameters for

the segment category, and performs the computation

as described in Section 4.2.

Figure 7: Schematic overview for the combination of node

and segment buffer objects (left) for a compact representa-

tion of street network geometry (right).

Stylization Parameters. The concepts presented in

Section 4.3 enables to reuse stylization parameter

sets. This is considered in our implementation by

encoding distinct sets using uniform buffers, e.g., in-

dexed by a street category and LoD.

Textures and Framebuffer Objects. The distance

textures are represented using single color channels

with 8-bit value precision and a resolution of 512

pixels. Further, 2D texture arrays are used for the

storage and access of the generated distance ﬁelds.

These texture arrays can be directly used for render-

to-texture. Figure 8 shows an exemplary conﬁgura-

tion of a framebuffer object. The individual layers

are aligned in descending order with respect to a seg-

ment’s rank. During the geometry pass, each layer

can be directly addressed for rasterization, and later

used for bilinear sampling. However, this requires the

texture arrays to have the same image resolution, i.e.,

of the viewport, and the same texture precision.

Figure 8: Exemplary render-to-texture conﬁguration for

multiple segment categories. Each render layer comprises

three render targets (width distance ﬁeld, respective length

distance-ﬁeld, and linear fragment depth).

5.2 Distance Field Computation

To enable an efﬁcient rendering performance, the

computation of the required distance-ﬁeld geometry

is performed within a single rendering pass. This

is achieved by using the combination of render-to-

texture (RTT), layered rendering, and multiple ren-

der targets (MRT) with separable blending functions.

The polygonal representation of each segment is com-

puted using a geometry shader with a constant prim-

itive output of six triangles. It also applies view-

frustum and back-face culling prior to rasterization.

Further, the shader maps network categories to their

respective layers stored in the framebuffer objects.

5.3 Stylization and Compositing

The ﬁnal stylization of a transportation network,

based on the generated distance ﬁelds, is performed

using fragment shaders in a subsequent compositing

pass. By rendering a screen-aligned quad, i.e., a

quad that covers the complete viewport, a fragment

shader computes for each render layer at each screen

pixel: (1) the respective LoD level and interpolates

the style parameters, and (2) subsequently performs

deferred texturing to compute the resulting color. Fi-

nally, bottom-up compositing yields the ﬁnal pixel

color (Porter and Duff, 1984).

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214

Level-of-Detail Computation. Multiple appro-

aches are possible to deﬁne how respective level-

of-details are computed within our prototypical

implementation. We basically distinguish between

three different LoD computation variants (see Sec.

6.1 for applications):

Global LoD. The LoD level is set at a global network

scale, i.e., it is equal for all segments in a 3D scene

and can be set using uniform variables. For exam-

ple, this enables explicit coupling of zoom levels

to levels of detail.

Distance-based LoD. The respective LoD level is

computed based on the distance of a fragment or

vertex to the virtual camera. The distance is com-

puted in linear eye-space, therefore it yields linear

interpolation of LoD levels. This approach en-

ables view-dependent LoD rendering and seam-

less transitions between different LoD.

Texture-based LoD. This approach is similar to the

distance-based computation, but the LoD level

is encoded using a texture, i.e., the LoD level

is determined using texture sampling. This en-

ables explicit LoD control by applying lens-based

metaphors in world, camera, and screen-space as

well as explicit deﬁnitions of region-of-interest

functions (Semmo et al., 2012).

It is also possible to combine different LoD com-

putations using a hierarchical approach, i.e., using a

global LoD value as a basis that can be reﬁned using

distance-based or texture-based LoD computations,

but which remains subject to future work.

Deferred Texturing. Given a respective style rep-

resentation S at a computed level of detail, deferred

texturing is performed. This limits the required com-

putations to visible fragments only and therefore re-

duces the workload of the per-fragment computations

of the rendering pipeline. Our system provides the

application of procedural or raster-based textures, de-

pending on the application. While simple coloring

can be achieved using 1D procedural texturing ac-

cording to distance ﬁelds, an additionally provided

length parametrization (Fig. 8) can be used to ap-

ply 2D raster-based textures (P1). For instance, fuzzy

or sketchy appearances can be achieved by using 2D

noise or stroke textures (Kopf et al., 2010).

Bottom-up Compositing. The evaluated per-

category colors are ﬁnally composited using a

bottom-up strategy (Porter and Duff, 1984). There-

fore, the colors are blended starting from the lowest

rank to the highest accordingly (P5/P6). This en-

ables the correct overlapping between the different

category ranks (P2) and alpha blending to visualize

tunnels or similar constellations. Depending on

the application, the blending procedure can take

different aspects and data into account (P3), e.g., to

consider only colors of the main segments and ignore

respective outlines (Fig. 6(c)).

5.4 Performance Evaluation

This sections brieﬂy discusses the runtime perfor-

mance of our prototypical implementation. We tested

our approach using OSM datasets of different geo-

metric complexity (Tab. 1). The performance tests

are conducted on the following test platform: Intel

i3-3110M (2,4GHz, Dual Core, Hyperthreading) with

Intel HD 4000 GPU running a Gentoo Linux (Ker-

nelversion 3.12.6). The test application runs in win-

dowed mode. The complete scene is visible in the

view frustum, thus view-frustum culling is not per-

formed but backface culling is enabled. For each mea-

suring step, a total of 5000 consecutive frames are

rendered. Finally, all records are averaged.

Table 2 shows the results of our performance eval-

uation for different screen resolutions. The mea-

sured run-time latencies clearly indicate that our ap-

proach is ﬁll-limited due to heavy per-fragment oper-

ations and limited by the number of applied parameter

sets. Considering the low-end graphics hardware and

non-optimized shader implementations, the achieved

frame rate of more than 20 frames-per-seconds satis-

ﬁes interactive time constraints.

Despite the GPU-based representation of a trans-

portation network and the respective parameter sets,

our image-based approach introduces additional costs

in video memory. Given the horizontal w and ver-

tical h framebuffer resolution, the required layers l

(e.g., distance, length, and depth), the precision in

Bytes b, as well as the number of street categories

c, the additional memory consumption O can be esti-

mated linearly with: O = c · w · h · l · b. For example,

approx. 214 MB video memory is required for a full

HD (1920×1080) rendering of a street network com-

prising nine categories with three render targets per

layers at a ﬂoating-point precision of 32 Bit. Since

distance ﬁelds are suitable for being interpolated lin-

early during sampling (Green, 2007), a precision of

Table 1: Geometric complexity of the test data sets used in

the runtime performance evaluation.

ID Data Set #Nodes #Ways

A Berlin 1 5571 1028

B Istanbul 2004 263

C Berlin 2 9502 1766

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215

Table 2: Performance results rendering the test data sets at

different output resolutions using different number of styl-

ization deﬁnitions (in milliseconds).

Resolution ID Parameter Sets

1 2 4 8

390 × 260 A 3.0 3.2 4.1 5.5

B 2.9 3.3 4.1 5.4

C 3.0 3.4 4.2 5.5

670 × 450 A 3.0 3.2 4.1 5.7

B 2.9 3.3 4.2 5.6

C 3.0 3.4 4.2 5.8

1280 × 800 A 25.5 29.0 36.1 50.1

B 25.4 29.0 36.2 50.1

C 25.3 29.2 36.2 50.2

8 Bit is sufﬁcient for most applications, resulting in a

memory consumption of approximately 50 MB.

6 RESULTS & DISCUSSION

This section presents a discussion of our results by

means of application examples and by discussing lim-

itations which lay the basis for future work.

6.1 Application Examples

We tested our approaches using different OSM data

sets with different categorizations for route styliza-

tion. Figure 9 shows an overview of various appli-

cation examples demonstrating the capabilities of our

prototypical rendering techniques.

Localization. The support of different stylization

parameters facilitates the generation of localized

maps (e.g., in terms of color (P7)) without requiring

to change the geometric representation. Figure 9(a)

shows three different view-dependent stylizations that

can be interchanged during rendering. Note how net-

work segments with lower rank are faded in the rear

part of the scene as well as the counterbalance of the

foreshortening of the segments’ widths.

Distance-based Stylization. In (Vaaraniemi et al.,

2011) the view-dependent rendering of routes are in-

troduced. This counterbalances the effects of 3D per-

spective projections such as perspective foreshorten-

ing, and thus visual clutter (Jobst and D

ollner, 2008)

in the rear parts of the 3D scene (Fig. 9(b)). This

can be achieved using the presented LoD approach

by enabling: (1) the fading of low-ranked route seg-

ments and (2) increasing the width of high-ranked

route segments with increasing distance to the virtual

camera (P3/P5). Thus, the depiction of low-ranked

or unimportant routes can be omitted to avoid vi-

sual clutter while important routes are emphasized to

counterbalance perspective foreshortening.

Interactive Lens-based Filtering. This fo-

cus+context functionality is common in visualization

frameworks for interactive user-driven ﬁltering (P3).

To support this feature, the LoD approach is applied

as follows (Fig. 9(c)): two distinct stylization param-

eter sets are deﬁned for the focus and context region

respectively. Here, the focus shows a detailed view on

a route network comprising all route categories, while

the context only depict the three major categories. A

screen-space lens (see inset) is used to control the

transition between the respective LoD levels.

Regions-of-Interest Visualization. Similar to lens-

based ﬁltering, the LoD approach can be used to

explicitly highlight a certain navigation route, while

omitting the rendering of the remaining network ar-

eas. Figure 9(d) shows an example of controlling

the level-of-detail stylization using a region of inter-

est (RoI) deﬁned along a path of network segments.

This can be used for highlighting segments relevant

for navigation. The RoI is encoded using a texture

(inset) that is referenced in network coordinates. The

rendering of network parts that are not of interest is

mostly omitted. A transition represented by a drop-

off function conveys parts of the context required for

navigation, e.g., junctions or routes with a high (im-

portant) rank.

6.2 Limitations

To this extent, the image-based approach presented

in this paper is limited conceptually and technically.

Despite being currently limited to render planar net-

works, the usage of distance ﬁelds causes two prob-

lems in the ﬁnal depiction: intrusion and protrusion.

Intrusion is caused if the distance ﬁeld of two routes

of the same category intrude each other because of

a large line width parametrization. Thus, the ren-

dering results can appear as being connected to the

viewer. Further, protrusion appears where two street

categories with different width parameter intersect,

especially at T-junctions. Here, the distance ﬁeld of

a high ranked street protrudes the distance ﬁelds of

a lower ranked street. Finally, the additional video

memory required by our approach is a major techni-

cal limitation, especially for future implementations

on mobile devices. With respect to these, future im-

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216

(a) OpenStreetMap, Bing, and Google Maps stylizations. (b) Distance-based Stylization.

Figure 9: Overview of different application examples rendered with the presented approach.

plementations for mobile platforms would require ge-

ometry shader functionality, e.g., in OpenGL ES.

6.3 Future Work

The presented approaches lay the basis for future re-

search directions. Despite optimizing the implemen-

tation and enhancing the visualization by integrating

internal labels (Vaaraniemi et al., 2014), the previ-

ously described limitations can be counterbalanced by

adapting the cap geometry generation according to the

node grade and the ranks of adjacent segments.

Further, our approach can be enhanced by apply-

ing adaptive or view-dependent tessellation or sub-

division schemes of the input line segments to yield

more smooth curves, e.g., for the rendering of round-

abouts. Furthermore, the geometry creation stage can

be extended by computing alternative geometric rep-

resentations, e.g., to enable the application of street

networks as visualization scenery. Also, the compact

network representation also lays the basis for future

research in view-adaptive generalization of transfor-

mation networks, solely performed on GPU.

7 CONCLUSIONS

This work presents an interactive, image-based ap-

proach for interactive rendering and cartographic styl-

ization of transportation networks that is especially

suitable for map visualization, which we exempliﬁed

for street networks. Our fully hardware-accelerated

rendering technique enables the efﬁcient storage of

route network geometry and appearance variances

while requiring only a single geometry and post-

processing pass for image synthesis. It represents the

basis for a number of applications in the context of

2D and 3D geovirtual environments, such as view-

adaptive rendering of route networks, interactive ﬁl-

tering as well as highlighting.

ACKNOWLEDGEMENTS

This work was funded by the Federal Ministry of Ed-

ucation and Research (BMBF), Germany within the

InnoProﬁle Transfer research group ”4DnD-Vis”.

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217

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