Woodification: User‐Controlled Cambial Growth Modeling

Kratt, Julian; Spicker, Marc; Guayaquil, A.; Fišer, Marek; Pirk, Sören; Deußen, Oliver; Hart, John C.; Beneš, Bedřich

doi:10.1111/cgf.12566

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…Buds use signaling by the growth hormone Auxin to prevent overgrowth and to control apical dominance [Kebrom 2017]. Secondary plant development (cambial growth) is the thickening of a tree trunk and branches [Kratt et al 2015] simulated by expanding their radii using da Vinci's rule (see Minamino and Tateno [2014] for a discussion).…”

Section: Developmental Plant Modelmentioning

confidence: 99%

Procedural Urban Forestry

et al. 2022

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The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework’s effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users.

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Section: Developmental Plant Modelmentioning

confidence: 99%

Procedural Urban Forestry

et al. 2022

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“…Recently, a number of methods started focusing on more accurately modeling the biomechanical and physical properties of plants [Pirk et al 2016]. This ranges from modeling the interactive growth of trees [Longay et al 2012;Pirk et al 2012a] and climbing plants , the interaction of plants and fluids, such as wind [Habel et al 2009;Quigley et al 2018], and fire , to simulating material properties with FEM methods [Wang et al 2017b;Zhao and Barbič 2013], and even the cambial growth of trees [Kratt et al 2015]. These methods provide computational models and representations that enable dynamic motions of plant geometries, thereby enhancing overall realism.…”

Section: Related Workmentioning

confidence: 99%

Learning to reconstruct botanical trees from single images

Li¹,

Kałużny²,

Klein³

et al. 2021

ACM Trans. Graph.

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even when the trees are captured in front of complex backgrounds. Moreover, although our neural networks have been trained on synthetic data with data augmentation, we show that our pipeline performs well for real tree photographs. We evaluate the reconstructed geometries with several metrics, including leaf area index and maximum radial tree distances.CCS Concepts: • Computing methodologies → Generative and developmental approaches; Shape analysis; Computer vision problems.

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“…Buds use signaling by the growth hormone Auxin to prevent overgrowth and to control apical dominance [28]. Secondary plant development (cambial growth) is the thickening of a tree trunk and branches [32] simulated by expanding their radii using da Vinci's rule (see [44] for a discussion).…”

Section: Developmental Plant Modelmentioning

confidence: 99%

Procedural Urban Forestry

Niese¹,

Pirk²,

Albrecht³

et al. 2020

Preprint

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Fig. 1. Steps of our learning-based plant population method: we use satellite images (a) and predict coverage maps for vegetation (b). We use these maps to identify plant regions for reconstruction (c) and to learn parameters for our procedural models when populating new virtual cities with complex plants (d), which significantly increases the realism of urban landscapes (e).The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or can be learned from satellite images and land register data. Together with approaches for generating buildings and trees, this allows us to populate urban landscapes with complex 3D vegetation. The effectiveness of our framework is shown through examples of large-scale city scenes and close-ups of individually grown tree models; we also validate it by a perceptual user study.CCS Concepts: • Computing methodologies → Shape analysis; • Theory of computation → Grammars and context-free languages; Rewrite systems.

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Woodification: User‐Controlled Cambial Growth Modeling

Cited by 18 publications

References 36 publications

Procedural Urban Forestry

Procedural Urban Forestry

Learning to reconstruct botanical trees from single images

Procedural Urban Forestry

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