Self-organizing tree models for image synthesis

Pałubicki, Wojciech; Horel, Kipp; Longay, Steven; Runions, Adam; Lane, Brendan; Mĕch, Radomír; Prusinkiewicz, Przemysław

doi:10.1145/1531326.1531364

Cited by 174 publications

(147 citation statements)

References 44 publications

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“…Therefore, we have modified the radiation model using the shadow propagation method (Palubicki et al 2009). We divide the space into a grid of voxels each with the associated shadow value S (x,y,z).…”

Section: Modelmentioning

confidence: 99%

“…Each segment is ascribed to a particular voxel creating a pyramidal penumbra at the voxels underneath. Shadow propagation length S L and voxel edge size V S determine, how many voxel layers the shadow propagates down (Table 1; Palubicki et al 2009). At the top voxel S(x,y,z) = S max , whereas at the bottom S(x,y,z) = 0 (linear interpolation between).…”

Section: Modelmentioning

confidence: 99%

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Data-based stochastic modeling of tree growth and structure formation

et al. 2016

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Data-based stochastic modeling of tree growth and structure formation Potapov I., Järvenpää M., Åkerblom M., Raumonen P., Kaasalainen M. (2016). Data-based stochastic modeling of tree growth and structure formation. Silva Fennica vol. 50 no. 1 article id 1413. 11 p. Highlights• We propose a stochastic version of the tree growth model LIGNUM for producing tree structures consistent with detailed terrestrial laser scanning data, and we provide the proofof-concept by using model-based simulations and real laser scanning data.• Trees produced with the data-based model resemble the trees of the dataset, and are statistically similar but not copies of each other; the number of such synthetic trees is not limited. AbstractWe introduce a general procedure to match a stochastic functional-structural tree model (here LIGNUM augmented with stochastic rules) with real tree structures depicted by quantitative structure models (QSMs) based on terrestrial laser scanning. The matching is done by iteratively finding the maximum correspondence between the measured tree structure and the stochastic choices of the algorithm. First, we analyze the match to synthetic data (generated by the model itself), where the target values of the parameters to be estimated are known in advance, and show that the algorithm converges properly. We then carry out the procedure on real data obtaining a realistic model. We thus conclude that the proposed stochastic structure model (SSM) approach is a viable solution for formulating realistic plant models based on data and accounting for the stochastic influences.

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Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Data-based stochastic modeling of tree growth and structure formation

et al. 2016

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“…Although technical users are capable of modelling many excellent distinct trees, rules are too abstract for novice users. Therefore, interactive sketch-based interfaces have been developed to generate 3D trees from sketched 2D shapes [15,39,41,54,33] during the past decades. The TreeSketch system [33] is a popular sketch-based iPad app.…”

Section: Related Workmentioning

confidence: 99%

High-quality tree structures modelling using local convolution surface approximation

2013

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In this paper, we propose a local convolution surface approximation approach for quickly modelling tree structures with pleasing visual effect. Using our proposed local convolution surface approximation, we present a tree modelling scheme to create the structure of a tree with a single high-quality quad-only mesh. Through combining the strengths of the convolution surfaces, subdivision surfaces and GPU, our tree modelling approach achieves high efficiency and good mesh quality. With our method, we first extract the line skeletons of given tree models by contracting the meshes with the Laplace operator. Then we approximate the original tree mesh with a convolution surface based on the extracted skeletons. Next, we tessellate the tree trunks represented by convolution surfaces into quadonly subdivision surfaces with good edge flow along the skeletal directions. We implement the most timeconsuming subdivision and convolution approximation on the GPU with CUDA, and demonstrate applications of our proposed approach in branch editing and tree composition.

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“…Procedural representations are able to represent complex structures easily, and so there has been a lot of research into the generation of three-dimensional models of trees by procedural-representation methods [7], [10]. However, because the shapes of the trees generated by these methods are unpredictable, it is not easy to obtain a tree of an envisioned style.…”

Section: Introductionmentioning

confidence: 99%

Energy-Based Tree Illustration System: ETIS

Nakajima

Mama

Morimoto

2016

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose a system named ETIS (Energy-based Tree Illustration System) for automatically generating tree illustrations characteristic of two-dimensional ones with features such as exaggerated branch curves, leaves, and flowers. The growth behavior of the trees can be controlled by adjusting the energy. The canopy shape and the region to fill with leaves and flowers are also controlled by hand-drawn guide lines.

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Self-organizing tree models for image synthesis

Cited by 174 publications

References 44 publications

Data-based stochastic modeling of tree growth and structure formation

Data-based stochastic modeling of tree growth and structure formation

High-quality tree structures modelling using local convolution surface approximation

Energy-Based Tree Illustration System: ETIS

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