OBJECTS VISUALIZATION IN DIGITAL TERRAINS USING

ADAPTIVE VIEW-DEPENDENT TECHNIQUES

Rafael Moreira Savelli, Roberto de Beauclair Seixas

Computer Graphics Technology Group, Pontiﬁcal Catholic University of Rio de Janeiro

Rua Marqu

es de S

ao Vicente 255, Rio de Janeiro, Brazil

Anselmo Antunes Montenegro and Esteban Walter Gonzalez Clua

Computer Institute, Federal Fluminense University, Rua Passo da P

atria 156, Niter

oi, Brazil

Keywords:

Terrain Visualization, View-dependent Techniques, Image-base Rendering.

Abstract:

There are many applications involving featured terrains visualization. One of them is for gaming purposes.

However, when dealing with real data, many of this applications fail to cope with such information. So,

this work presents an efﬁcient way to minimize or even remove this kind of problem. We propose a method

based on image-based rendering and view-dependent visualization techniques. These methods were applied in

terrain visualization with real vegetation data where distribution and type is determined from digital satellite

images.

1 INTRODUCTION

There are many problems related to real-time visual-

ization and simulation of featured terrains and some

of these problems were already solved by a large

number of computer graphics researchers. However,

the problem of efﬁciently rendering featured terrains

is still a challenge as recent demands for larger and

more complex terrains are leading to an increase in

the number of objects to be rendered. Depending on

the current amount of objects to be rendered on each

frame, the ﬁnal results can be unsatisfactory because

many applications are not well prepared to deal with

such amounts of information.

Nowadays we can ﬁnd many terrain visualization

and simulation applications that require the display of

objects with dense distribution. In many cases, the

objects are vegetation data which include forests with

many different elements as trees, bushes, grass and

so on. Examples of works that have investigated the

problem of visualization of vegetation data on terrains

are (Lluch et al., 2004; Jakulin, 2000; Dietrich et al.,

2005). In this work we also deal with the problem

of rendering terrain with dense vegetation and part of

the motivation is from the problem investigated in a

previous work (Savelli and Beauclair, 2006).

In (Savelli and Beauclair, 2006), a satellite im-

age was used extensively to help composing a cer-

tain visualization. First of all, the authors used the

own satellite image as the terrain texture. Later, the

authors have shown how to classify different kinds

of vegetation using only information provided by the

satellite image by applying a method based on the

combination of wavelets representation and the split-

and-merge algorithm for grouping similar informa-

tion. The solution that was proposed was able to place

a large number of elements on the considered terrain

in a semi-automatic way.

The work presented here suggests a natural con-

tinuation based on the previous results where a sim-

ple, but efﬁcient method, integrates a group of visu-

alization techniques based on image-based rendering

with level of detail representations (Akenine-Mller

and Haines, 1998). The aim is to solve the problem

of terrain rendering with a large amount of vegeta-

tion data. To be more speciﬁc, we propose a scheme

that combines multi-resolution billboards representa-

tion with a mechanism for object rendering in which

density may vary according to related camera position

and viewing angle. In addition, a hierarchical struc-

ture is used in order to represent, in an efﬁcient way,

billboards collections placed on terrain.

The document structure is organized as following:

in section 2 we discuss some important fundamentals

and some relevant works associated to them; in sec-

tion 3, we describe the proposed method giving a brief

379

Moreira Savelli R., de Beauclair Seixas R., Antunes Montenegro A. and Walter Gonzalez Clua E.

OBJECTS VISUALIZATION IN DIGITAL TERRAINS USING ADAPTIVE VIEW-DEPENDENT TECHNIQUES.

DOI: 10.5220/0001802003790386

In Proceedings of the Fourth International Conference on Computer Graphics Theory and Applications (VISIGRAPP 2009), page

ISBN: 978-989-8111-67-8

explanation on the ﬁrst sub-section and a more de-

tailed one on the following sub-sections; in section 4

results are presented from the implementation of the

methods described in section 3. In section 5 we dis-

cuss probable future works. Finally, in section 6 we

present a conclusion and some considerations.

2 MODELING TERRAIN AND

OBJECTS

In general, interactive terrain visualization algorithms

are rather complex. For this reason, in the last

decade, the subject has received attention from many

researches from all around the world. Hence, many

strategies and solutions were developed since then.

In computer graphics, a terrain is basically a

graphical object where a surface is used as the main

geometrical support. Commonly, this surface is rep-

resented as a triangulated network where every sin-

gle point has attributes and information provided from

one or more texture maps. So, a given geographical

region is modelled as a graphical three-dimension ter-

rain composed by a triangulated network and a geo-

referenced texture. The ﬁnal result is an empty ter-

rain with no objects on it. All user can see is some

elevation and geographical accidents like rivers and

lagoons.

2.1 Terrain Level of Detail

In many cases, terrain data are represented by a huge

amount of triangles and textures with very high res-

olution. In such conditions, simpler strategies are no

longer capable to deal with all information for real

time interaction. In order to avoid this problem, other

sophisticated methods must be used. Such meth-

ods must take into consideration the properties of the

scene as well as some intrinsic characteristics of vi-

sualization processes as existing spatial and temporal

coherence in the environment.

Most actual methods agree that, in real time vi-

sualization algorithms, the more distant from the ob-

server a given region is, the less reﬁnement is neces-

sary. This is a usual way to cut off unnecessary pro-

cessing without harming the visualization quality.

The need for different levels of reﬁnement have

led developers to use a hierarchical structure where

every element represents a small terrain fragment in

a certain level of detail. Frequently, these structures

determine a recursive terrain subdivision producing

many regions that can be regular or irregular. In ad-

dition, it is necessary to deﬁne one or more schemes

which are capable of determining the best represen-

tation, for each region, as a function of distance and

observer position. In order to determine the level of

detail used, these schemes evaluate the projection er-

ror between the chosen level and the most reﬁned

one (absolute error metric). Or optionally, between

the chosen level and the reﬁnement level immediately

above in the same considered hierarchy (relative error

metric).

Among all proposed works involving level of de-

tail terrain visualization, two of the most signiﬁcant

are those from (Lindstrom and Pascucci, 2001; Lind-

strom and Pascucci, 2002). The terrain visualization

method implemented in this work follows the same

principles and ideas described in both references (Po-

yart et al., 2002) and (Lindstrom and Pascucci, 2002).

2.2 Image-Base Object Modeling

Featured terrains are commonly useful for a very large

application range such as simulations and games. The

most difﬁcult task consists in dealing with both terrain

and object data because the latter can be very complex

and numerous. A good example is a large terrain with

very dense vegetation on it.

Likewise in the case of empty terrains, it is also

possible to apply the same ideas and approaches of

view-dependent level of detail to every single object

spread all over the terrain surface. However, in some

cases like, for example, forest visualization, the num-

ber of objects can be so huge that even variable level

of detail geometric modelling may be insufﬁcient to

keep interaction in usable rates. In such cases, a rea-

sonable alternative is to change completely or par-

tially the representation from geometric models to

image-based rendering structures.

Nowadays there are many image-based render-

ing visualization techniques including some simple

ones like sprites and billboards and others much

more sophisticated that in practice require implemen-

tation in Graphic Processing Units (GPUs), like depth

sprites (Pharr and Fernando, 2005) and relief textures

(Oliveira et al., 2000).

2.2.1 Sprites and Billboards

Sprites are graphical objects described, in most cases,

by planar textured surfaces. The given texture is ba-

sically a snapshot from an object (real or synthetic)

taken from a given point of view. In many cases, two

textures are placed in a perpendicular way in order to

produce a better approximation to 3D shapes as, for

example, in the representation of trees.

A billboard is a graphical object similar to a sprite,

however, differently from it, a billboard must rotate

GRAPP 2009 - International Conference on Computer Graphics Theory and Applications

380

itself in order to always face the observer, producing

the illusion of a three-dimensional effect in the visu-

alization process (McReynolds and Blythe, 1998).

Supposing that we are only interested in image-

base structures which require the less possible com-

plexity, we tested both sprites and billboards strate-

gies in a essay to ﬁgure out the most adequate one.

Figure 1 shows a little group of trees using only

sprites while Figure 2 shows only billboards trees.

Figure 1: Trees represented as sprites.

Figure 2: Trees represented as billboards.

In the beginning, we expected that sprite struc-

tures would require less computational effort than

billboard structures. However, practical tests have

shown exactly the opposite, frustrating our initial ex-

pectation. As we could observe, at each frame, draw-

ing sprites is usually much more expensive than draw-

ing the equivalent billboard. It became evident that

graphical processing units can draw a textured poly-

gon, evaluate a rotation matrix and ﬁnally, deal with

all basic operations faster than drawing two static tex-

tured polygons. As we can see in Table 1 and graphi-

cally in Figure 3 when adding more and more trees to

a given terrain, the frame-per-second taxes based on

sprite structures reduced faster than when using bill-

board structures.

Table 1: Billboards vs sprites performances.

Number of Frame-per-seconds:

elements: Billboards: Sprites:

50 40.0 40.0

100 40.0 40.0

200 33.3 25.0

300 28.6 20.0

400 25.0 16.7

Figure 3: Billboards vs sprites graphic performances.

All values in Table 1 were obtained by running a

very simple terrain visualization in a 1.6 GHz Intel 4

processor with 512 MB RAM memory and 256 MB

GeForce FX5200 video graphic card.

As a consequence of the results, we decided to use

a billboard-based strategy as the main representation

structure in our method. However, sophisticated tech-

niques like depth sprites and relief textures can be also

used for more realistic results.

It is important to say that another strategy that

was considered is the representation of vegetation el-

ements by models generated by procedural strategies

as, for example, L-Systems, using geometry shaders.

Although such strategy seems promising, the use of

geometry shaders has not produced competitive re-

sults yet, when large number of objects must be ren-

dered. The cause for such poor performance is due

to limitations in the buffer elements in current archi-

tectures. In the near future, it is expected that this

limitations will vanish as it is becoming evident with

the new generation of graphics hardware.

3 THE PROPOSED METHOD

All proposed techniques in this section are simple

but efﬁcient and belong to the group of image-based

and view-dependent visualization problems. They

OBJECTS VISUALIZATION IN DIGITAL TERRAINS USING ADAPTIVE VIEW-DEPENDENT TECHNIQUES

381

are based in two main fundamental ideas: ﬁrst, the

use of different image resolutions to represent three-

dimensional objects. Second, a technique which en-

ables the variation of the density of the vegetation dis-

tribution, depending on the distance to observer.

3.1 Overview

In a previous work, using methods and practices cited

in (Savelli and Beauclair, 2006), we distribute some

vegetation types all over the terrain by computing

their positions from satellite images. Here, for every

vegetation element, we built a set of textures from ﬁve

viewing angles in different digital resolutions. They

include front, back, left, right and top views. In run-

time and given a point of view, it is possible to create,

dynamically, a radial partition of the terrain into three

different regions in which we associate different lev-

els of density for the vegetation distribution. The idea

is to use less elements if the distance to the observer

is sufﬁciently large., use a medium density for the in-

termediary regions and ﬁnally, draw all elements in-

dividually in regions close to the observer.

Once decided which density to use, we select one

of the ﬁve viewing angle billboards according to the

camera orientation. Next, we must choose the appro-

priated resolution level according to distance between

camera and billboard. So, on the following subsec-

tions, we describe in details every necessary step to

compose the ﬁnal visualization.

3.2 Billboards Construction

An important key to get an acceptable object repre-

sentation is to pick up a good texture to it. In the

Internet, there are many images that could be used as

tree textures, however, we opted to generate textures

from three-dimension polygonal models. The reason

to do so is simple and it is based on the idea that,

once you have the model, the creation of all discussed

viewing angles become more controllable and ﬂexi-

ble increasing the chances to match our expectations

in the process of texture creation.

In this work, we capture texture images of objects

to create billboards. For that task, a software com-

ponent was integrated to our visualization application

in order to read and render three-dimensioned models

described in .obj format in a separated frame buffer.

This technique is also called frame buffer objects.

We also know that billboards are used with much

more efﬁciency when objects have axial symmetry.

When such symmetry is not present, billboards use

tend to be limited. A classical technique called impos-

tors (Schauﬂer, 1995) consists in generating dynamic

billboards just for small scene parts where approxima-

tion error is signiﬁcant. In this work, we do not use

impostors and instead, we propose set of billboards

having a single texture for every orthogonal projec-

tion located in the top semi-hemisphere as shown in

Figure 4.

Figure 4: Billboards set and its orthogonal projections.

In our implementation, we get ﬁve textures where

each one represents a different viewing angle of an

object. In case, those ﬁve orthogonal textures are:

front, back, left, right and top. In the visualization

process, we select the billboard that has the mini-

mum difference between the creation angle and the

viewing one. Even with rotation transforms being ap-

plied to billboards, a special care is required in or-

der to guarantee continuity when textures must be

swapped. This could be solved by using 3D-warping

techniques (McMillan, 1997) as we know all depth

maps involved in such operation.

3.3 Elements Density Determination as

a Function of Distance

It is known that, the more distant to observer an ele-

ment is, the less vegetation details can be noticed. We

have simulated this notion by discarding some very

distant vegetation elements, saving CPU processing

in rendering time.

In Figure 5 we can identify three distinct vegeta-

tion groups with different distances between them and

the observer. The ﬁrst group is the closest group to the

observer, located in the bottom of the ﬁgure. The sec-

ond group is around the middle of the ﬁgure, in an in-

termediary distance to observer. Finally, group three

can be found on the extreme top part of ﬁgure being

the most distant from all the analyzed ones. Looking

at each individual group is is possible to notice how

densities may vary according to distance.

In order to get the presented effect, we adopted

the common level-of-detail technique. This technique

is very useful to simplify three-dimensional model

networks as described in (Chamberlain et al., 1996;

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382

Figure 5: Billboards with adaptive density.

Funkhouser and Squin, 1993). In our case, level-of-

detail was adapted to cut off some very far away bill-

boards. The point is to do this in a smooth way with-

out harming the visualization. For that, we divided

the not culled terrain part into three different regions

with distances d

, d

and d

to the observer accord-

ing to Figure 6. We decided, by experiments, that for

a good visualization effect we must display 100% of

elements in the ﬁrst group. In the second one, only

80% are shown. Finally, in the last group, we have

to cut off 50% of elements. After distance d

, no ele-

ments are displayed.

Figure 6: Mult-texture billboard distribution as function of

distance.

3.4 Multi-Resolution Billboard

In order to increase even more the frame rates, we

also include a multi-resolution representation for ev-

ery created billboard. In Figure 7, there are two bill-

boards with different resolutions. The left one has a

256x256 pixels texture while the right tree has only a

64x64 pixels texture.

The main idea consists in cutting off unnecessary

processing changing texture resolution dynamically

and at running time. To get the most efﬁciency, we

must attach higher quality textures in billboards near

observer and lower quality textures in billboards far

enough from observer.

In our case of study, we work with three distinct

resolution textures as described in (Jakulin, 2000).

Figure 7: Mult-texture billboard distribution as function of

distance.

Resolutions are 64x64, 128x128 and 256x256 pixels.

Figure 6 shows how we select texture resolution as a

function of distance d

, d

and d

3.5 Objects Grouping

The multi-resolution billboard method and the vari-

able density distribution mechanism yielded nice re-

sults, however, the ﬁnal visualization did not reﬂect

precisely the real distribution as presented in satellite

images. To avoid this sort of problem, other tech-

niques must be used, such as level of detail and adap-

tive grouping.

Here, we consider that, if some vegetation element

is sufﬁciently distant from the observer, the angular

variations to make its billboard face the viewer would

be so small that a set of elements should be repre-

sented as a unique group displayed in a single bill-

board. Thus, depending on the distance and angle

visualization we can display now a set of objects as

being a single billboard placed onto the terrain, in-

stead of having only individual billboards. For this,

we propose then a new method based on collection of

imagens in which an hierarchical structure is used to

deal with different types of objects grouping.

The objects grouping method applied in this work

uses a special data structure based on a quadtree

where each node can store a group of images taken

from different points of view. The ﬁnal quadtree has

not only individual objects but also groups of objects.

By construction, leaf nodes contain only individual

objects while internal nodes contain representations

of object groups. The set of nodes which have the

same parental node can be represented, in a speciﬁc

point of view, by a single image where all nodes ap-

pear together forming a unique group. Consequently,

the collection of graphical objects is represented by

an adaptive hierarchical structure.

OBJECTS VISUALIZATION IN DIGITAL TERRAINS USING ADAPTIVE VIEW-DEPENDENT TECHNIQUES

383

3.5.1 Quadtree Structure

To build the quadtree structure, an hierarchical par-

titioning of the entire terrain domain must be done,

where each cell (or node) represents a group of ob-

jects in a given level.

The subdivision method is done as following: we

start with the root node containing the whole domain

of the terrain. If that node contains more than one ob-

ject, then it must be divided into four equal parts. Oth-

erwise, the subdivision for that node is done. For each

leaf-nodes we associate references to a set of prede-

ﬁned textures containing all views such as front, back,

left, right and top. This must be done for every veg-

etation type used in the visualization. In Figure 8,

we show a very small example where two kinds of

vegetation elements are placed on the terrain. Note

that, from bottom to top, internal nodes’ textures are

built based on their child-nodes. So, groups can be

formed by individual objects, by other groups of ob-

jects or even by a synthetic polygonal objects, in the

case they are available. The process is ﬁnished when

a predeﬁned quadtree level is reached where a single

billboard will be hold too many objects. At this point,

the visualization will be compromised and this strat-

egy is no longer applicable.

Figure 8: Object grouping representation using billboards.

Note that having only one element in the leaf-

nodes is not the only possible solution. It is also pos-

sible to have leaf-nodes represent small groups of ob-

jects. In cases where terrains have a very dense veg-

etation, we could set an arbitrary amount of elements

as being the minimum displayed group. Those group

distributions would be based on the local average dis-

tribution that can be computed using basic statistical

methods.

3.5.2 Selecting Representation Level

In order to have the quadtree structure working on a

visualization process, a strategy is required for select-

ing the best representation level to use in a speciﬁc

terrain region from the hierarchical billboard collec-

tion structure.

This selection must be based on the projection er-

ror which depends on factors like distance and an-

gle of visualization. A reasonable error metric can

be evaluated as proposed by (Schauﬂer, 1995) in his

work about dynamic impostors.

Given a point of view, we must evaluate the pro-

jection error metric for each node starting from the

root to the leaf-nodes. If the billboard (with single

or multiple objects) has an error metric less than a

given threshold, then we use that billboard to repre-

sent the whole objects in that region. Otherwise, we

descend a level and repeat the same procedure until

we reach a node in which the error is acceptable. In

the worst case, all individual elements will be ren-

dered and displayed, giving us the worst frame per

second rate. However, in practice, this situation will

only occur in regions close to observer. In the other

ones, a single billboard will appear showing a group

of elements and saving lots of time in rendering many

objects.

An important comparison must be done about the

proposed idea and the impostors technique. Unlike

the impostors technique, here it is not necessary to

recompute the three-dimensional objects’ representa-

tion every time they are considered invalid. The main

idea here is to use the representations with the largest

groups of objects as possible according to the consid-

ered threshold. When this is not possible, the algo-

rithm must descend the quadtree nodes, searching the

ﬁrst acceptable level of representation.

In order to illustrate how our structure can be used

in the selection of different level of detail, we pre-

pared a very simple prototype using only groups of

one, two or three billboards for a small and constant

bunch of three trees. The results from that prototype

are shown in Figure 9. When the observer is far away

from the given bunch of trees, only one billboard with

three-trees texture is used. For this example, that is

what we call the lowest level of representation. As

the observer comes closer to that bunch, the method

switches into two billboards now using two-trees and

single-tree textures. Then, the selection process con-

tinues until all trees in the example are represented in

different billboards using single-tree textures only. In

that situation, the highest level is now reached.

As seen previously, by grouping objects using the

proposed structure, we can render a larger amount of

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384

Figure 9: Simple example of representation level using

quadtree.

objects with a less number of physical billboards. As

a consequence, when using such strategy, we expect

to render larger scenes in a more efﬁcient way. To

conﬁrm this statement, we compared the FPS rate in

both strategies (with and without objects grouping).

For test purposes, we used a simple list of objects as

data structure instead of a quadtree when no objects

grouping are used in the visualization process. This

simple structure appears to be useful in our tests and

the performance results can be observed in Table 2.

Table 2: With and without objects grouping.

Objects

List Quadtree

billboards FPS billboards FPS

100 100 85,9 60 90,3

200 200 69,3 125 75,9

300 300 58,5 166 67,6

400 400 50,7 219 60,9

500 500 45,0 266 55,7

600 600 39,9 327 49,4

700 700 36,2 394 45,0

800 800 33,0 479 40,0

900 900 30,3 531 36,9

1000 1000 27,9 641 33,0

4 RESULTS

Every technique presented so far, yields a slight im-

prove in the processing performance. However, the

greatest enhancement arises when all techniques are

combined and applied simultaneously.

Final results can be observed with a reasonable de-

gree of realism and interactive rates. Figures 10 and

11, at the end of this document, show the ﬁnal visual-

ization application in two different regions of a given

terrain. Note that the frames per second (FPS) rate ap-

pear in the top left image corner. With no techniques

seen in this paper, the frames per second rates would

be around 0.02 for the same terrain and number of

objects.

An important point is that it is not always possible

to have the perfect equivalence between both strate-

gies. We support the idea that, if the visualization

of the environment involves mainly groups that are

rather distant to the observer, then the designed ap-

proach becomes a very good approximation and vi-

able solution.

5 FUTURE WORKS

As future works, we propose a bunch of improve-

ments that solve some of the problems that appeared

in the presented techniques.

In the current implementation, when a billboard is

beyond a maximum distance, the application stops re-

painting it on every frame and the object disappears

from the terrain. An alternative approach to avoid this

abrupt change consists in using a technique called fad-

ing where imminent billboards would be removed in

a gradual way. Another improvement would consider

new techniques to minimize transitions between bill-

board views. In that case, a three-dimension warp-

ing strategy seems promising. Another interesting

point is to take into consideration non-photo-realistic

rendering techniques which can improve information

perception in the visualization process.

6 CONCLUSIONS

We presented in this work some techniques based

on three-dimension object representation by images

and view-dependent visualization. Those techniques

helped to handle very large terrains with densely dis-

tributed vegetation data . This work is also based on

real data extracted from satellite images.

All presented techniques here are simple but ef-

ﬁcient and have shown us how important is to con-

sider view-dependent strategies in featured terrains,

even with all the advanced hardware resources avail-

able nowadays.

We realized that such work is important not only

considering the direct results but also understanding

OBJECTS VISUALIZATION IN DIGITAL TERRAINS USING ADAPTIVE VIEW-DEPENDENT TECHNIQUES

385

its uses in different contexts. Considering the fact

that we have vegetation information provided from

digital satellites images, this tool could be very use-

ful for helping environment governmental ofﬁces and

departments to monitor and control devastation in

some critic regions. Considering military purposes,

we could use this tool as a simulator attending part of

new ofﬁcials training process.

Figure 10: Final composition with 7357 objects using 1824

physical billboards.

Figure 11: Final composition with 10211 objects using

2479 physical billboards.

ACKNOWLEDGEMENTS

We take this opportunity to thank the staff of both

the Federal Fluminense University and the Institute

of Pure and Applied Mathematics for their kind help

and support in many technological project aspects. A

special thanks for the CNPq and Faperj ﬁnancial pro-

grammes that made possible this work.

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