1. Data-driven authoring of large-scale ecosystems
- Author
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James Gain, Adrien Peytavie, Eric Galin, Eric Guérin, Konrad Kapp, University of Cape Town, Origami (Origami), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), ANR-16-CE33-0001,HDWorlds,Modèles procéduraux paramétriques pour la représentation d'univers virtuels complexes(2016), ANR-20-CE23-0001,AMPLI,Mondes virtuels vastes : apprentissage et modélisation procédurale inverse(2020), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)
- Subjects
Canopy ,Sunlight ,Computer science ,Climatic adaptation ,natural phenomena ,020207 software engineering ,Terrain ,02 engineering and technology ,Understory ,15. Life on land ,computer.software_genre ,Computer Graphics and Computer-Aided Design ,[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] ,shape modeling ,Data-driven ,Computer graphics ,0202 electrical engineering, electronic engineering, information engineering ,Leverage (statistics) ,Gap dynamics ,Ecosystem ,Data mining ,computer ,Undergrowth - Abstract
In computer graphics populating a large-scale natural scene with plants in a fashion that both reflects the complex interrelationships and diversity present in real ecosystems and is computationally efficient enough to support iterative authoring remains an open problem. Ecosystem simulations embody many of the botanical influences, such as sunlight, temperature, and moisture, but require hours to complete, while synthesis from statistical distributions tends not to capture fine-scale variety and complexity. Instead, we leverage real-world data and machine learning to derive a canopy height model (CHM) for unseen terrain provided by the user. Trees in the canopy layer are then fitted to the resulting CHM through a constrained iterative process that optimizes for a given distribution of species, and, finally, an understorey layer is synthesised using distributions derived from biome-specific undergrowth simulations. Such a hybrid data-driven approach has the advantage that it incorporates subtle biotic, abiotic, and disturbance factors implicitly encoded in the source data and evidences accepted biological behaviour, such as self-thinning, climatic adaptation, and gap dynamics.
- Published
- 2020
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