Texture Based Flow Visualization in Augmented and Virtual Reality Environments.
ABSTRACT Virtual and Augmented Reality allows a new way of result exploration of numerical simulations, its analysis and interpretation by immersing the user into the data sets and/or by keeping the relationship and context to the real environment. Up to date only a few approaches targeting the deployment of augmented reality during the analysis stage have been published. New texture based visualization tools emerged and conciliate a better understanding of the flow fields. This paper describes our approach for integrating texture based visualization methods within a mixed reality set-up by introducing a 'low cost', fully 3D interactive post processing unit for CFD data sets.
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ABSTRACT: This paper introduces a new augmented reality framework based on a multi-source urban planning backbone aiming at interactively investigating fast "what-if" analysis of urban planning simulations and creating awareness of possible environmental impact. The process of advanced urban planning nowadays includes the simulation of physical phenomena, its analysis, visualization and interpretation in order to evaluate the impact on the layout of the planning. For example, noise and air pollution, annoyances due to both nearby transportation infrastructure and urban traffic have become a serious concern for citizens. In order to provide major aid to the involved stakeholders, especially city managers, new techniques for the preparation, representation and interpretation of the typically large amount of resulting simulation data are required. These must be designed in order to enhance the perceptual and cognitive processes of users to facilitate faster interpretation and decision making. Hence, we introduce a new augmented reality framework which not only allows users to visualize but also to analyze physical phenomena in fast "what-if"-scenarios. By changing boundary conditions, parameters and re-simulating at interactive rates results can be augmented into real world planning layouts. Augmenting reality, urban planning and layouts with resulting simulation data through real-time visualizing tools provides a new and efficient interactive post processing unit for the exploration and analysis of the environmental impact due to changing conditions.Proceedings of the 2010 Spring Simulation Multiconference, SpringSim 2010, Orlando, Florida, USA, April 11-15, 2010; 01/2010
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ABSTRACT: Mobile augmented reality applications are in need of tracking systems which can be wearable and do not cause a high processing load, while still offering reasonable performance, robustness and accuracy. The motivation to develop yet another tracking algorithm is two-fold. Most of the existing approaches use classical optimization techniques such as the Gauss-Newton method. However, since those algorithms were developed to address general optimization problems, they do not fully exploit the structure of the pose estimation problem with its geometric constraint targets. Also, mixed reality applications demand that pose estimation be not only accurate but also robust and computationally efficient. Hence there is a need for algorithms that are as accurate as classical algorithms, yet are also globally convergent and fast enough for real-time applications. In this paper we introduce a new iterative geometric method for pose estimation from four co-planar points and we present the current status of PTrack, an infrared marker-based single camera tracking system benefiting from this approach. Our novel pose estimation algorithm identifies possible labels composed of retro-reflective markers in a 2D post-processing using a divide-and-conquer strategy to segment the camera's image space and attempts an iterative geometric D reconstruction of position and orientation in camera space. Tracking results are made available to applications through OpenTracker [OpenTracker 2006] framework. To analyse tracking accuracy and precision, we built a generic test-bed and compared PTrack to ARToolKit [Kato and Billinghurst 1999; Kato et al. 2000], one of the most wide-spread low-cost tracking solutions. Results show that our tracking system achieves competitive accuracy levels better than ARToolKit and close to commercial systems, while being highly stable and affordable.Proceedings VRCIA 2006 ACM International Conference on Virtual Reality Continuum and its Applications, Chinese University of Hong Kong, Hong Kong, China, June 14-17, 2006; 01/2006
Texture-Based Flow Visualization in
Augmented and Virtual Reality Environments
Paul Benölken Holger Graf
Fraunhofer Institute for Computer Graphics
Fraunhofer Str. 5
64283 Darmstadt, Germany
paul.benoelken | holger.graf | email@example.com
Virtual and Augmented Reality allows a new way of result exploration of numerical simulations, its analysis and
interpretation by immersing the user into the data sets and/or by keeping the relationship and context to the real
environment. Up to date only a few approaches targeting the deployment of augmented reality during the
analysis stage have been published. New texture based visualization tools emerged and conciliate a better
understanding of the flow fields. This paper describes our approach for integrating texture based visualization
methods within a mixed reality set-up by introducing a ‘low cost’, fully 3D interactive post processing unit for
CFD data sets.
flow visualization, texture based algorithms, virtual reality, augmented reality.
The numerical analysis and visualization of flow
phenomena has become an indispensable part of
many different research and application fields. VR
based solutions as the pioneering work of Bryson
[Brys92] or the flow field analysis presented in
[Schu99] have shown how to ease the understanding
and interpretation of complex 3D data sets.
For visualization vector fields vector fields, glyphs,
path-, stream, streak lines or particle animations are
commonly used. Nevertheless these methods are
either limited to local vector field analysis or provide
a rather coarse spatial resolution. Hence even in
combination with advanced
techniques relevant flow characteristics may be
missed without any deeper knowledge of the flow
field. This problem is addressed by texture-based
visualization methods, which provide means for
displaying the global characteristics of a flow field in
an intuitive way with a sufficient resolution and
Virtual reality systems increase the efficiency during
the analysis stage but still lack the integration with
real environments. Here AR offers a challenging and
promising technology enabling the engineer to
environment in which he works.
In this paper we present our solution for integrating a
texture-based visualization method into a virtual and
augmented reality environment. Our application
combines traditional methods for visualizing vector
fields with the Oriented Line Integral Convolution
approach introduced by Wegenkittl et.al.[Wege96].
The presented system is based on a ‘low cost’
solution of a client server architecture and runs on a
results to the real
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2. Related Work
The visualization of flow fields has been subject of
numerous developments and publications. Hauser et
al [Haus02] give an extensive overview and
categorization of existing solutions and recent
Alternatively to integral objects, texture-based or
dense integration methods as the spot noise method,
introduced by van Wijk [Wijk91] were successfully
applied for visualizing vector fields. Line integral
convolution (LIC), first published by Cabral and
Leedom [Cabr93] is a very popular and powerful
method for visualizing dense vector fields.
With their Fast LIC method Stalling and Hege
[Stal95] obtained a significant improvement in LIC
performance by exploiting the coherence along
streamlines. An additional improvement was
achieved by the Integrate&Draw method [Pere98],
which produces images with more contrast and a
time saving of ~ 50% compared to Fast-LIC.
Forsell and Cohen [Fors95] extended LIC for
curvilinear surfaces with animations techniques,
directional and magnitude information. The LIC
extension presented by Teitzel et al. [Teit97] works
on 2D unstructured grids as well as on triangulated
surfaces in 3D. With their OLIC approach
Wegenkittl et al. [Wege97] address the problem of
visualizing the orientation of the flow. Extensions
were made for speeding up this method (FROLIC)
and for animating the flow via the internet [Berg00].
For extending LIC to 3D Interrante [Inte97] presents
a combined approach of direct volume rendering and
LIC. Rezk-Salama et al. [Resk99] use 3D textures
mappings for achieving interactive performance
A significant performance gain was achieved with
the image based flow visualization technique (IBFV)
introduced by van Wijk [vanWijk 2002]. Laramee et al.
[Lara03] recently extended this method for flow field
visualizations on surfaces. However this technique
has not yet been integrated into an AR or VR
In fact, research efforts for interactive exploration of
simulation results for flow fields in an AR set-up just
started to evolve. To our knowledge, the generation
of texture based visualization methods for an
interactive exploration of flow fields in a mixed
reality set-up has so far not been presented. Most
applications are placed within the area of medicine,
architecture, edutainment or cultural heritage. An
overview can be found e.g. in [Azum01].
The closest work to this publication has been
investigated within the ARVIKA project [ARVI]. To
overcome the limitations of current simulation
packages namely the loss of the relationship to the
real environment Regenbrecht
[Rege02] make use of augmented reality to lead ‘on-
site’ inspections of simulation results within an
3. Texture based Flow Visualization
Beside the conventional technique for interactively
computing and displaying 3D stream curves (lines,
tubes, faces) isosurfaces and cross sections (see
[Beno03]) we implemented an algorithm for
generating and mapping flow textures onto polygonal
surfaces like cross sections or isosurfaces.
As the orientation of the underlying vectors is an
important feature for
understanding the 3D flow characteristics we
modified and extended the fast oriented LIC
(FROLIC) method for polygonal surfaces.
The original FROLIC algorithm operates on regular
grid structures where the topological information is
given implicitly. Hence, for efficiently processing
triangulated surfaces additional spatial data structures
are required. In our implementation we used a binary
hash table for storing the surface edges and enabling
a fast access on adjacent triangles. Furthermore the
triangles are mapped into local Euclidian space for
achieving an efficient point localization, rasterization
and integration. A 4th order Runge-Kutta scheme is
applied for performing the numerical integration. As
the computation of streamlines on triangulated
surfaces implies multiple crossings of triangle
boundaries the integrator needs to check whether the
current triangle has been left or not. From the
Euclidian representation of the triangles we
computed barycentric coordinates for fast point
Beside the flow orientation within the polygonal
mesh the orientation of the 3D vectors with respect to
the surface has to be reflected for achieving a correct
spatial visualization of the flow. Thus, vectors with a
tangential orientation should cause longer traces as
orthogonal vectors. This is accomplished by
adaptively computing the filter length Li by applying
the following exponential function:
the perception and
Here |v2D| is the length of the projected 2D vector
and |v3D| denotes the 3D vector length. Lmax is
the user defined maximal filter length.
The selection of seed points is another critical issue
in computing streamlines. Especially for the oriented
LIC method the initial positions of the droplets have
to be selected carefully for avoiding overlaps, which
cause artifacts in the output image and performance
losses. We evaluated different strategies for selecting
seed points (see Table 1). For the random selection
we implemented a modified sobol scheme, which
delivered the best performance with minimal
The animation of the original FROLIC method is
either accomplished by a cyclic variation of the
streamlet intensity or by color-table animation.
Nevertheless additional filter operations have to be
applied for suppressing pulsation effects.
Our implementation for animating FROLIC images
is based on a variation of seed points for each frame.
The seed points of frame i are shifted with a constant
factor along the streamlines. These output seed
points are used as an input for the streamline
computation in the subsequent frame i+1.
For this purpose the seed point selection is prepared
within two steps: First, a set of seed points is selected
using the random selection mechanism. Afterwards, a
subset from the computed set of seed points is
selected by evaluating the neighborhood of a seed
point such that no other streamlet is found in a
The augmented reality setup we used for exploring
the flow field of a car is shown in figure 1. The user
is wearing a head mounted display with two
integrated micro cameras. The video see-thru mode is
accomplished by placing a video texture onto the
backplane of the virtual environment. An A.R.T.
Dtrack system is used for tracking the user’s head-,
PIP-, pen- and model positions with high quality.
This system uses retro-reflective markers, which are
tracked by two self-flashing infrared cameras.
In our client server architecture [Beno03], the
rendering client was running on a 1.2GHz Windows
PC with 512MB main memory, a PNY NVIDIA
Quadro 750 Graphics board. The compute server was
running on a 2GHz PC with 2GB main memory.
The interactive generation of the texture based
visualization methods can be achieved in providing
parameters, such as resolution and filter length,
transparency, number of animation images, etc., for
the manipulation of the texture based image
generation can be interactively modified using 3D
direct manipulation widgets as illustrated in Figure 3
Some results of the work described so far are shown
in the figures 1 and 2. Figure 1 shows a texture based
flow-field visualisation using a FROLIC –texture for
the representation of the vector field. This texture is
mapped onto a cutting plane, whereas figure 2 shows
the same visualization method mapped onto an iso-
The system set-up has been tested with different data
sets in different sizes and different resolutions. To
investigate the flow field interactively, a fast retrieval
of the starting (‘seed-‘) points is inherent for
streamline computations at interactive rates. The
following table (table 1) shows the performance of
the texture based streamline computations using
different strategies for setting the seed points.
Dataset ScanLine Diagonal Block Random
371ms 361 ms 380 ms 341 ms
7971 ms 7942 ms 7992 ms 7612 ms
260 ms 271 ms 270 ms 218 ms
2624 ms 2654 ms 2533 ms 2508 ms
Table 1: Seed-point selection performance
Within this paper we presented our solution for
texture based flow visualization on triangulated
surfaces. State of the art texture based visualisation
algorithm have been modified and extended for
polygonal surfaces. The system set-up for an
Figure 2: Mapping flow textures on isosurfaces.
3D manipulation widgets are used for parameters changes.
Figure 1: Texture based flow field visualization in AR setup.
The users view is displayed in the right top corner.
interactive exploration of flow fields in a mixed
reality scenario is promising new application areas
within the engineering domain.
Faster computations will be achieved by image space
based methods. However, as the order of operations
(texturing, projection) completely differs from
conventional approaches, the integration into a
video-mixing AR environment is still an open issue.
Alternatively, higher frame rates might be achieved
by exploiting more flexible programmable graphic
boards such as the NVidia FX series. Moreover,
within an augmented reality set-up using video-
mixing technology, the used framegrabber boards
still impose a bottleneck in respect to performance
and image synchronization between the video image
and the generated virtual objects. Our future work
concentrates on the enhancement for texture-based
visualisations at interactive rates and optimize the
augmented reality set-up exploiting more hardware
features offered by the new generation of graphic
This work is part of an EU-funded project called
ViSiCADE (IST – 2000 – 28123). We would like to
thank our project partners in the consortium for the
support in the developments of this prototype.
Furthermore we would like to address the
studierstube team (esp. Dieter Schmalstieg) being
thankful for the provision of the source code and
support during the last months.
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