FROM VOLUME SPLATTING TO RAYCASTING

A Smooth Transition

Guillaume Gilet, Jean-Michel Dischler

Laboratoire des Sciences de l’Images, de l’Informatique et de la T

etection

Universit

e Louis Pasteur, Bd Sebastien Brant, Illkirch, France

Luc Soler

Institut de Recherche contre le cancer de l’appareil digestif, 1 Place de l’hopital, Strasbourg, France

Keywords:

Volume Rendering, Point-Based Rendering, Raycasting.

Abstract:

Splatting-based methods are well suited to render large hierarchical structured or unstructured point-based

volumetric datasets. However, as for most object-order volume rendering methods, one major problem still

remains the processing of huge amounts of elements usually necessary to represent highly detailed densely

sampled datasets, which can lead to poor performances. Resampling into 3D textures to apply hardware-based

raycasting is a common way of improving framerate in such cases but often with a certain precision loss related

to limited texture memory. Switching between these two rendering techniques is interesting in order to keep

the advantages of both and has thus been proposed before, but not in a smooth and hierarchical manner. In this

paper, we show that for most point-based volumetric datasets, some parts of the model can be rendered more

efﬁciently with a texture-based method, whereas other parts can be rendered as usual using splatting. We ad-

dress the issue of providing a smooth hierarchical transition between the two methods using a data-dependent

approach based on a per-pixel ray-driven rendering scheme. In practice, our transition scheme allows users

a good control of the performance/quality trade-off. Through a comparison with standard hierarchical EWA

splatting, we show that our smooth transition can lead to an improvement of framerate without introducing

visual inconsistencies or artifacts.

1 INTRODUCTION

Volume rendering is a useful technique for the efﬁ-

cient visualization of volumetric datasets. Whether

these datasets result from simulations, from measure-

ments of some physical processes or from modern

3D scanning devices, they are very often expressed

as irregularly sampled point clouds. The most nat-

ural method to visualize these point-based datasets

is splatting, ﬁrst introduced by Westover (Westover,

1989). The splatting process reconstructs a continu-

ous ﬁeld from the sampled scalar ﬁeld using 3D re-

construction kernels associated with each scalar value

and is therefore well suited for direct rendering of

large unstructured point-based datasets. However,

like all object order algorithms, such methods are

often bound by the inherent point complexity thus

showing poor performance for highly detailed and

very densely sampled datasets. Resampling such

datasets into 3D textures in order to use a hardware-

accelerated raycasting scheme is a common method

for real-time visualization. Whereas such a texture-

based scheme offers an efﬁcient interactive visual-

ization tool, its usefulness can be hampered by sev-

eral constraints, such as GPU texture memory limita-

tion. It is therefore often impractical to use a texture-

based volume visualization tool for large point-based

datasets.

Since each method has its strengths and draw-

backs, it seems to be an interesting idea to devise a

hybrid scheme using an appropriate combination of

these methods, in order to increase performance. Sev-

eral methods have been introduced to allow the vi-

sualization of large unstructured datasets using such

a hybrid scheme. However, whereas these methods

achieved efﬁcient results, few relied on an effective

combination scheme of both rendering principles with

easy and efﬁcient user control of visual results when

switching between both.

In this paper, we introduce a new method based

on an efﬁcient combination of the EWA (Elliptical

Weighted Average) volume splatting framework and

hardware-accelerated raycasting. Basically, we pro-

pose a scheme aiming at a smooth transition be-

217

Gilet G., Dischler J. and Soler L. (2008).

FROM VOLUME SPLATTING TO RAYCASTING - A Smooth Transition.

In Proceedings of the Third International Conference on Computer Graphics Theory and Applications, pages 217-222

DOI: 10.5220/0001096002170222

 SciTePress

tween a ﬂexible point-based method and texture-

based hardware-accelerated methods. The main idea

is to render each part of a dataset using an efﬁcient

combination of point/texture-based methods. Con-

trary to other transition methods, we do not propose

a binary choice between the two methods, but rather

an efﬁcient combination of these methods, thus max-

imizing rendering performance. Our algorithm also

takes full advantage of the latest hardware accelera-

tions. In practice, our method improves the framer-

ate of splatting without introducing visual inconsis-

tencies.

The remainder of this paper is structured as fol-

lows. Section 2 discusses some related works. Next,

we present our combination scheme and simpliﬁca-

tion criteria. Finally, before concluding, results and

an analysis of our method are discussed in section 4.

2 PREVIOUS WORKS

Direct volume rendering methods have in common an

approximative evaluation of the volume rendering in-

tegral for each screen pixel, i.e. the integration of at-

tenuated colors and extinction coefﬁcients along each

corresponding viewing ray. Color and extinction co-

efﬁcients are computed by a classiﬁcation step. Clas-

siﬁcation is achieved by means of a transfer function

which maps scalar values s = s(x) of the dataset to

colors c(s) and extinction coefﬁcients τ(s). By as-

suming that the viewing ray x(λ) is parameterized by

λ the distance to the viewpoint, the classical volume

rendering integral can be written as:

I =

c(s(x (λ)))exp



−





dλ



dλ

(1)

with D being the maximum distance.

The continuous data ﬁeld is usually represented by a

discrete function with values at vertices along with

an interpolation scheme, based on a reconstruction

kernel. This reconstruction kernel has a great impact

on image quality and signal reconstruction accuracy.

Two main approaches can be distinguished for direct

volume rendering : Forward projection methods and

backward projection methods. Whereas in forward

projection methods rays are cast from the image into

the volume ((Roettger et al., 2003)), backward projec-

tion methods map volume elements onto the screen

((Zwicker et al., 2001),(Cohen, 2006)). An overview

of recent volume rendering methods can be found in

(Kaufman and Mueller, 2005). Several of these algo-

rithms have now been successfully modiﬁed to take

beneﬁts from latest graphics hardware ((Ma et al.,

2003) presents a brief overview of several hardware-

accelerated volume rendering methods).

Several volume splatting algorithms focus on im-

proving the image quality, such as (Mueller et al.,

1999), (Neophytou et al., 2006) and (Neophytou

and Mueller, 2003). Xue and Crawﬁs (Xue and

Crawﬁs, 2003) compared several hardware accelera-

tions for splatting algorithms, showing the efﬁciency

of shader-based methods for large datasets render-

ing. The popular EWA (Elliptical Weighted Average)

Splatting ((Zwicker et al., 2001), (Chen et al., 2004))

provides an efﬁcient framework for interactive splat

based volume visualization. However, in order to ob-

tain correct and high quality images with splatting

methods, splats must often be depth-sorted, resulting

for most highly complex and densely sampled models

in a severe impact on performance. There are several

techniques to improve splatting performances by re-

ducing the number of splats to be projected ((Mueller

et al., 1999),(Laur and Hanrahan, 1991)), but with

some limitations.

To overcome issues of splatting methods when

dealing with densely sampled datasets and issues of

raycasting methods when rendering large unstruc-

tured datasets, hybrid methods are a natural solution

((Ma et al., 2002)). In a similar manner, Wilson et

al. introduced in (Wilson et al., 2002) a hardware-

assisted hybrid scheme using semi-opaque splatting

in conjunction with a texture-based method using a

uniformly subsampled version of the dataset. Re-

cently, Kaehler proposed in (Ralf Kaehler and Hege,

2007) an efﬁcient hierarchical hybrid representation

for the storage and rendering of large unstructured

datasets.

These methods basically propose a choice be-

tween using a point-based or a texture-based method

or both to render a subpart of a dataset. In contrast,

we propose in this paper a hybrid method based on an

effective combination of these techniques through the

use of a smooth transition between both.

3 FROM SPLATTING TO

RAYCASTING : A UNIFORM

REPRESENTATION

This section describes our new hybrid rendering

scheme. The idea is to render the volumetric dataset

with a hybrid splatting/raycasting method.

As highlighted in (Meissner et al., 2000), we have

on the one hand a splatting method efﬁcient to render

sparse structures and on the other hand a hardware-

based raycasting suited to render densely sampled

GRAPP 2008 - International Conference on Computer Graphics Theory and Applications

218

datasets. Therefore, the motivation of our method is

to obtain a framework allowing for the rendering of

a dataset using a combination of both methods, thus

attaining higher performance. In order to achieve an

Figure 1: Principle of the method. An element is projected

onto the screen plane, yielding a Gaussian 2D footprint. De-

tails are added to each pixel with ray marching inside the

corresponding 3D texture.

efﬁcient combination of both object-order and image-

based methods, we need a uniform representation of

these techniques. To this end, we choose to deﬁne our

new hybrid technique as composed of two steps :

• A splatting step, corresponding to the correct pro-

jection of 3D kernels into the screen, yielding 2D

footprints.

• A reﬁnement step, i.e. the computation of a color

value for each pixel of the corresponding foot-

print.

As one can see, both classical rendering schemes can

be straightforwardly mapped to this representation,

i.e. classical raycasting methods being the projection

of a single 3D kernel bounding the dataset and de-

tailed through a raycasting algorithm, classical splat-

ting methods being the projection of a collection of

3D kernels with a unique value for each kernel.

Figure 1 shows the principle of our method. The

key idea is to render the dataset as a collection of

overlapping 3D detailed kernels. These kernels can

be obtained by resampling the dataset at a given rate

and assigning a 3D texture and an interpolation kernel

to each sample point. The resampling rate of a dataset

and the resolution of the corresponding 3D texture are

key control-features of our hybrid scheme and must

be deﬁned for each region (contiguous subpart) of the

dataset. To this end, we provide a hierarchical repre-

sentation of the input volumetric dataset, where each

node is a coarse representation of a region and is asso-

ciated with a 3D texture containing the necessary in-

formation for the rendering of the associated region.

As shown in ﬁgure 2, a subpart of this hierarchical

representation is selected at run time and rendered us-

ing a GPU-accelerated splatting scheme. Details are

then added to each footprint using raycasting princi-

ples with the 3D texture associated to the node. The

composition of each detailed footprint yields the ﬁnal

image.

We explain in the following subsection the actual

rendering method of a set of overlapping detailed 3D

kernels. Details concerning the hierarchical represen-

tation are given in subsection 3.2.

Figure 2: Hierarchical representation. Each node repre-

sents its child nodes and is associated with a 3D texture.

3.1 Hybrid Rendering

As described before, the integration of raycasting

principles into the splatting method must be carefully

analyzed in order to ensure consistent results with an

improvement of performance.

At each node of our hierarchical structure is

attached a truncated spherical interpolation function

(a radially symmetric Gaussian in our case). For each

fragment f

i,x,y

of a footprint F

at screen coordinates

x, y resulting from the projection of a Gaussian

kernel G

, a ray is cast through the fragment into the

volume. We focus on the intersection of this ray with

the kernel G

. Since G

is in our implementation a

truncated radially symmetric Gaussian, its bounding

box B

can be seen as a sphere centered at the original

sample point. The ray originating at fragment f

i,x,y

intersects the bounding sphere in two points p

and

(with p

= p

in extreme cases).

We then use a raycasting scheme into the adequate

3D texture stored in the graphics hardware to enhance

the appearance of f

i,x,y

by adding details along the

segment p

. Through a careful analysis of the

3D texture resolution and the extent of the truncated

kernel G

, we can derive an adequate sampling step

along the segment, and thus divide the segment p

into n equal sections of length l.

Each of these segments is associated with a

color and opacity value using a lookup into a 2D

table computed through a classical pre-integrated

classiﬁcation scheme (Engel et al., 2001). Due

to the speciﬁcity of our scheme, segment lengths

may vary along with the size of the kernel. To be

able to rely on the 2D table lookup and still ensure

correct results, we use the approximation scheme

proposed by (Roettger et al., 2000) and weight the re-

sult of the 2D lookup with the length l of the segment.

FROM VOLUME SPLATTING TO RAYCASTING - A Smooth Transition

219

3.2 Hierarchical Representation

Figure 3: Creation process of our hybrid representation.

Groups of points meeting the criterion are represented by

a single kernel. The information of the original region are

stored into the GPU texture memory.

We choose to simplify the volumetric dataset using

an octree as in (Laur and Hanrahan, 1991). The in-

put volumetric dataset is divided into a collection of

hierarchical nodes, each node representing a subpart

of the volumetric dataset. A feature-sensitive top-

down approach is applied during the construction of

the point hierarchy.

Our representation is based on a division crite-

rion E. This criterion represents relevant features

and properties of a subpart of the dataset. This cri-

terion is used to determine the parameters of our hy-

brid method, such as the sampling rate of the dataset,

the resolution of the 3D textures, thus orienting our

rendering scheme toward splatting or raycasting for

each subpart of a dataset. We propose in this paper

two different criteria described in sections 3.2.1 and

3.2.2. Each region approximated by a single node (i.e.

meeting the criterion requirement) is stored into a 3D

texture of equivalent resolution. This simpliﬁcation

scheme yields a simpliﬁed representation of the vol-

ume coherent with our hybrid rendering solution.

3.2.1 Visual Variation Criterion

As highlighted in (Meissner et al., 2000), splatting

and raycasting methods produce slightly different vi-

sual results. This difference is the main cause of pop-

ping artifacts in most hybrid methods. The aim of our

method being a smooth artifact-less transition, these

visual differences should be minimized. Intuitively,

we want to resample and orient toward a raycasting

solution the regions which will induce low visual vari-

ations. In order to detect such regions, we need to

deﬁne a qualitative error metric measuring the visual

variation between the two schemes. This variation v

is expressed for a viewing ray r traversing the vol-

ume. We choose to deﬁne this variation as the dis-

tance in color space between two colors computed

along the viewing ray r by evaluating the volume ren-

dering integral (1) using the two different approxima-

tion schemes (splatting and raycasting):

= |C

splat

−C

raycast

| (2)

This visual variation reﬂects the visual differences be-

tween the splatting and raycasting solution for a sin-

gle viewing ray and can be considered as a measure-

ment of visual artifacts.

We choose to express the visual variation V over

a complete contiguous region as the average value of

the squared variation along all possible viewing rays

traversing this region :

V =

Ω

∂Ω (3)

with Ω the sphere of all possible view directions.

We propose to approximate this integral by using a

discrete sum evaluated over a small subset N of view-

ing rays determined by using a stochastic distribution

of viewing rays across a region.

Although the precomputation of the transfer table

used by the ray casting scheme in section 3.1 can be

easily computed, thus allowing the transfer function

to be edited on the ﬂy, our variation measurement

scheme is more complex. As of this moment, it is

not possible to modify the transfer function and keep

an interactive rendering, even for a small value of N.

For some applications however, it can be desirable to

edit the transfer function on the ﬂy. To this end, we

provide another division criterion, thus allowing an

interactive simpliﬁcation of the dataset.

3.2.2 Data Variance Criterion

The idea is to use data variance inside a region as a

decision criterion for our simpliﬁcation. Intuitively,

regions of a dataset with high variance will introduce

more visual differences. It is then compared with a

user deﬁned threshold, deﬁned empirically as of now.

Whereas this criterion is plainly less efﬁcient than the

visual variation measurement criterion, it allows for

an interactive simpliﬁcation. It is to be noted that this

variance analysis can be performed with or without

taking the transfer function into account. Nonethe-

less, this criterion gives rather good and often sufﬁ-

cient results for most datasets.

Figure 4: Tree Model. Left : Traditional splatting. 8M

splats - 2.2 fps. Middle : Our hybrid representation. Right :

Hybrid method with detailed textures. 46k splats - 8.3 fps.

GRAPP 2008 - International Conference on Computer Graphics Theory and Applications

220

4 RESULTS

This section deals with a study of visual quality and

performance of our method. We compare our hybrid

rendering obtained with different parameters with the

EWA splatting rendering of the same volumetric data.

We show results on both structured and unstructured

datasets. Timings were collected on an Intel Core 2

Duo Processor at 2.4Ghz and a GeForce 8800 graph-

ics card using a viewport resolution of 512 × 512. As

can be seen on ﬁgure 4 (right), our hybrid method al-

lows the rendering of datasets with a low splats count,

yet maintaining the same visual result as with tra-

ditional EWA splatting (left). This induces an in-

crease in performance over traditional splatting with-

out quality loss. Our visual variation criterion can

lead for most models to an efﬁcient reduction (50-

80%) of the point count necessary to render a dataset

with almost the same visual quality. Figure 5 shows

the impact of different user-deﬁned quality tresholds

on quality and framerate of our method. Naturally,

as the treshold grows higher, performance increases

while quality is degraded in some areas. On ﬁgure

6, we show the differences of the two criteria respec-

tively described in sections 3.2.1 and 3.2.2. While

most features are preserved, areas with low variation

(across the neck of the model) are degraded with the

variance data criterion but are accurately represented

using the visual difference criterion. Figure 7 shows

the same model rendered with and without texture de-

tails. Our method also induces an increase of framer-

ate for unstructured datasets, as shown in ﬁgure 8.

Figure 5: A 512

head dataset. Left : 2M splats - ε = 2%

- 2.9 fps; Middle : 386k splats - ε = 5% - 4.8 fps; Right :

250k splats - ε = 10% - 8.8 fps.

5 CONCLUSIONS

We presented in this paper a hardware-accelerated

hybrid volume rendering scheme. Our method pro-

vides a smooth transition between texture-based and

point-based techniques. Through an adequate divi-

sion of the dataset, each region of the dataset can be

rendered with an efﬁcient combination of both meth-

ods. Furthermore, we provided efﬁcient division cri-

teria for the analysis and simpliﬁcation of a dataset

according to user-deﬁned parameters controlling the

quality/speed trade-off. This technique leads to a

Figure 6: 512

Head Model rendered with 530k splats us-

ing the two criteria. Left : Visual Variation criterion. Right

: Variance data criterion.

smooth transition between two traditional principles

and an increase in performance over traditional splat-

ting methods for high quality rendering of point-based

volumetric datasets.

Several issues can be improved. In the future, we

want to increase the efﬁciency of our framework. First

of all, the use of a different hierarchical structure

(such as RBF-based representation) can allow for a

representation more ﬁtting to data, thus allowing for

both performances and visual quality increase. An-

other improvement is to derive the idea of adaptive

hardware accelerated EWA splatting ((Chen et al.,

2004)) to further enhance framerate. However, such

an improvement is not trivially devised in our case.

An adaptive scheme can be used to affect, not only the

quality of the reconstruction kernel, but also the sam-

pling step of the raycasting scheme. Furthermore, an

adaptive criterion should be devised while taking into

account our visual variation metric and several impor-

tance factors, such as the size or visibility of a region

(as a measurement of visual importance of a region on

the screen), as to enhance the splats count reduction

without introducing visual artifacts. As it is, visual

variations over a region are bound by our metric, but

these variations are accumulated onto the screen by

different locally variation-bound regions. To this end,

a thorough analysis of our metric can be proposed to

lead to a better adjustment of parameters and provide

an approximation of the total visual variation on the

ﬁnal resulting image.

Figure 7: A 512

medical dataset. Left :Our hybrid method.

2M splats at 2.1 fps. Right : Without texture details. 2M

splats at 12.3 fps.

FROM VOLUME SPLATTING TO RAYCASTING - A Smooth Transition

221

Figure 8: An unstructured ﬂuid dataset obtained through

simulation MAC. Left : splatting method - 1.5M splats - 11

fps. Right : our method - 250k splats - 16 fps.

ACKNOWLEDGEMENTS

The medical dataset was gratefully provided by IR-

CAD : www.ircad.fr.

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