Computergraphics and Nature
| dc.contributor.author | Neubert, Boris | en_US |
| dc.coverage.spatial | Konstanz, Germany | en_US |
| dc.date.accessioned | 2015-01-21T06:54:24Z | |
| dc.date.available | 2015-01-21T06:54:24Z | |
| dc.date.issued | 2012-05-07 | en_US |
| dc.description.abstract | <p>This thesis presents new methods for modeling and efficient rendering of botanical scenes and objects. The first methodallows for producing 3D tree models from a set of images with limited user intervention by combining principles ofimage- and simulation-based modeling techniques. The image information is used to estimate an approximate voxelbasedtree volume. Density values of the voxels are used to produce initial positions for a set of particles. Performing a3D flow simulation, the particles are traced downwards to the tree basis and are combined to form twigs and branches.If possible, the trunk and the first-order branches are determined in the input photographs and are used as attractorsduring the particle simulation. Different initial particle positions result in a variety, yet similar-looking branchingstructures for a single set of photographs. The guided particle simulation meets two important criteria improvingcommon modeling techniques: it is possible to achieve a high visual similarity to photographs and at the same timeallows for simple manipulations of the resulting plant by altering the input photographs and changing the shape ordensity, providing the artist with an expressive tool while leveraging the need for manual modeling plant details.Following paper based on guided particle simulations coined the term self-organizing tree models.The second method improves the concept of sketch-based modeling tools for plants. The proposed system convertsa freehand sketch of a tree drawn by the user into a full 3D model that is both, complex and realistic-looking. This isachieved by probabilistic optimization based on parameters obtained from a database of tree models. Branch interactionis modeled by a Markov random field, which allows for inferring missing information of the tree structure and combiningsketch-based and data-driven methodologies. The principle of self-similarity is exploited to add new branchesbefore populating all branches with leaves. </p><p>Both modeling methods presented in this work, produce very complex tree models. While this richness is neededto model highly realistic scenes, it leads to a complexity that makes real-time rendering impossible. We presentan optimized pruning algorithm that considerably reduces the geometry needed for large botanical scenes, whilemaintaining high and coherent rendering quality. We improve upon previous techniques by applying model-specificgeometry reduction functions and optimized scaling functions. We propose the use of Precision and Recall (PR) asa measure of quality to rendering and show how PR-scores can be used to predict better scaling values. To verifythe measure of quality we conducted a user-study allowing subjects to adjust the scaling value, which shows that thepredicted scaling matches the preferred ones. Finally, we extend the originally purely stochastic geometry prioritizationfor pruning in order to account for a view-optimized geometry selection, which allows to take global scene information,such as occlusion, into consideration. We demonstrate our method for the rendering of scenes with thousands ofcomplex tree models in real-time.</p> | en_US |
| dc.format | application/pdf | en_US |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/8289 | |
| dc.language | English | en_US |
| dc.publisher | Neubert | en_US |
| dc.title | Computergraphics and Nature | en_US |
| dc.type | Text.PhDThesis | en_US |
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