9.2 Scalar Data Visualization
9.2.3 Time-Dependent Data
Since ecosystems are not static, but rather exhibit complex dynamics over time, the analysis of their evolution, based on the evaluation of data at different points in time,
9.3. IMPLEMENTATION 95 is a valuable source of information. We are also able to handle time-dependant data and thus visualize the evolution of an ecosystem over time. A time-dependant data set is usually given as a set of scalar or label fields:
S : [t0, t1. . . tN]×D⊂R2 →R (9.3) It can be visualized using the same mapping technique described above, the only additional requirement being a proper interpolation function over time. We use linear interpolation on the time axis and thus tri-linear interpolation in the whole data set, as it is readily available in graphics hardware. Using this interpolation, one can compute the values of the data set at a given time and visualize it as in the previous section:
S(t) = (1−ut)Sti+utSti+1 (9.4) ti ≤t < ti+1
ut= t−ti ti+1−ti
9.3 Implementation
The mapping of scalar or label fields onto visual features as described above is well suited for our rendering pipeline, since all visual features can be either seamlessly controlled by parameters in the range [0.0. . . 1.0] or specified by the user as textures (hatch and silhouette strokes). However, as described in detail in 8, visual controls are implemented in different stages of the rendering pipeline, which in turn influences the way data is made available to the pipeline. Among the features shown in Table 9.1, abstraction is mostly performed in the early stage of the pipeline (vertex shader) while the other are done in post-processing.
While, of course, feeding the scalar field at the beginning of the pipeline would ensure that all features can be used for mapping, this raises serious complications:
sampling the scalar data from inside the vertex shader is not straightforward and would put a significant burden on the rest of the pipeline. Thus, also given the fact that the goal is not an exhaustive system, but rather exemplary visualization, we decided to only use the post-processing stage for data visualization, leaving a more complete implementation for an eventual commercial system.
In this case, data can be presented as textures in the graphics card memory, benefiting from the fast sampling and processing of the graphics hardware. The work flow is presented in Fig. 9.2 and uses the depth buffer to perform a projective texturing of the scene. For each pixel in the frame buffer, the computations are performed in the following steps:
• using the frame buffer depth data, a 3D position in camera space is computed by applying the inverse of the projection matrix.
• the 3D location in world space is computed by applying the inverse of the modelview matrix.
Figure 9.2:Embedding non-visuals in the landscape: the depth buffer is used to retrieve the world-space position at each pixel in order to projectively map a scalar field onto the scene (on the color attribute in this example).
• projective texturing is used to sample the data texture at the 3D location. The projection is similar to that used for terrain (orthogonal, on the z axis pointing upwards).
Obviously, the first step introduces numerical precision issues, as both depth and 2D pixel coordinates are discrete (limited in precision). However, the introduced artifacts are practically invisible, because the subsequent processing does not change the view and projection transformations and thus the sampling density is preserved.
Using the technique described above results in scalar data being made available to the post-processing stage of the rendering pipeline which, in turn, maps this data onto visual features accordingly.
9.4 Results
In this section, we have partially explored the potential of sketchy landscape visual-ization to be usefully combined with scientific visualvisual-ization of abstract environment data. Not only that such abstract representations leave sufficient room for additional visual information to be blended in, but they also lend themselves quite well as trans-porters of such information.
The key property that allow this kind of usage is the independent control of the vari-ous visual features, which can be thus correlated with scalar data values. The affinity of different visual features to various data semantics has been analyzed and should be taken into account in order to obtain meaningful visualizations.
The implementation in the sketchy rendering pipeline is lightweight and could be chosen such that only the final stage is affected. The data to be visualized is fed as a
9.4. RESULTS 97
Figure 9.3:Views of a scene. From top to bottom, left column: photorealism, 3D sketch, scalar field(via a colormap). Right column:triple scalar field using different hatch stroke color; two scalar fields using color and hatching; two scalar fields using color and silhouette style.
texture in the post-processing pixel shaders, where it is mapped onto visual features controlled by the user. Thus, the whole process is hardware-accelerated and also supports time-dependant data visualization.
Of course, in a practical visualization system it is possible and desirable to also in-corporate explicit, traditional visualization techniques, such as volume rendering or illuminated field lines, which also fit well in interactive sketches. We expect signifi-cant benefits for such an integrated visualization approach.
C HAPTER 10
C ONCLUSION
In this research work, we have investigated alternative approaches to real-time vi-sualization of complex ecosystem models and associated environmental data on the basis of interactive sketches. Our framework is able to render realistically complex, GIS-generated scenes according to the user’s specification with respect to abstraction degree and different visual styles that can be seamlessly integrated together, as well as with photorealistic renditions. Furthermore, visualization of environmental data can be embedded in the same interactive visualization.
Our primary motivation has been to create a practically usable test bed for land-scape professionals who need versatile visual tools and media in landland-scape analysis, planing, architecture etc. Particularly the communication and implementation of en-vironmental development projects and measures has been plagued by the inefficiency of existing computer-aided visualization means.
We have investigated traditional media in the landscape work-flow, the hand-drawn sketches, which are established and thus can serve as a source of inspiration. Then, their counterpart in the computer graphics, namely non-photorealistic rendering, has been reviewed and relevant existing algorithms have been adopted or adapted. Gaps in our target work-flow as well as problems of existing algorithms have been iden-tified and new techniques or improved versions of existing algorithms have been developed where necessary.
Specifically, we have covered the following fields:
Non-photorealistic rendering We have presented a framework for real-time illus-tration of complex landscape scenes that provides the user with the ability to control the appearance and drawing style of the scene by merely changing a few parameters.
Re-modeling with high-level primitives enables meaningful simplification as well as novel on-the-fly parametrization techniques for efficient stylization of silhouettes and real-time hatching of plants with spatial stroke coherence and reduced "shower door"
effect. The usual artistic usage of sketchy visual features becomes secondary. Rather, style and features should play the role of information carriers in conveying ideas and communication instruments. Moreover, photorealism and non-photorealism are no longer separated, but naturally integrated in the same rendering style palette.
Geometry processing and level-of-detail. A versatile billboard representation, com-puted in a pre-processing step, allows efficient rendering of complex plant models in both PR and NPR.
Data visualizationhe ability to handle large scenes opens interesting possibilities for the visualization of scalar data on landscapes, which is hardly possible within (too) complex photorealistic imagery.
It is in the nature of any research that, while attempting to clarify and propose solu-tions to certain problems, it also raises new quessolu-tions during the process - our work makes no exception in this respect. There are many aspects that can be improved, like a more efficient implementation (advances in graphics hardware will help) as well as more sensible abstraction mechanism that should come closer to human-like visual abstraction. A solution to the latter problem that we plan as future work is a segmentation of the scene according to relevant vegetation characteristics. We have also recognized new directions of research that are open. An interesting one is the automatic generation or selection of style and artwork textures according to vegeta-tion characteristics: this would remove the task of artwork creavegeta-tion from the user’s responsabilities and lead to faster workflow.
Due to its strong inter-disciplinary character, this work has been performed in a tight connection with experts and potential users in the landscape professional world.
However, as for any new work tool, the acceptance in practice will be shown in time. To ensure usability, the developed techniques have been integrated within a GIS-based landscape visualization system, Lenne3D. The ultimate goal is increased efficiency and acceptance of nature and environment development processes through enhancement of computer-aided tools, to which I therefore hope to have contributed.
A PPENDIX
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