research-article

Data-driven authoring of large-scale ecosystems

Authors:

Adrien PeytavieAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 39, Issue 6

Article No.: 217, Pages 1 - 14

https://doi.org/10.1145/3414685.3417848

Published: 27 November 2020 Publication History

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.

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Index Terms

Data-driven authoring of large-scale ecosystems
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 39, Issue 6

December 2020

1605 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3414685

Editor:
Karol Myszkowski
MPI Informatik

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Publication History

Published: 27 November 2020

Published in TOG Volume 39, Issue 6

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Cited By

Liu JZhang SZhang CZhang SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Controllable Procedural Generation of LandscapesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681129(6394-6403)Online publication date: 28-Oct-2024
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Kokosza AWrede HGonzalez Esparza DMakowski MLiu DMichels DPirk SPalubicki W(2024)Scintilla: Simulating Combustible Vegetation for WildfiresACM Transactions on Graphics10.1145/365819243:4(1-21)Online publication date: 19-Jul-2024
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Huang XRitschel TSeidel HMemari PSingh G(2023)Patternshop: Editing Point Patterns by Image ManipulationACM Transactions on Graphics10.1145/359241842:4(1-14)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592418
Li WChen XWang JChen B(2023)Patch-Based 3D Natural Scene Generation from a Single Example2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01608(16762-16772)Online publication date: Jun-2023
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Shen RMa CDong DXiao S(2023)Light Accumulation Map for Natural Foliage Scene GenerationAdvances in Computer Graphics10.1007/978-3-031-50072-5_37(467-478)Online publication date: 28-Aug-2023
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Pałubicki WMakowski MGajda WHädrich TMichels DPirk S(2022)EcoclimatesACM Transactions on Graphics10.1145/3528223.353014641:4(1-19)Online publication date: Jul-2022
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You JHuai YNie XChen Y(2022)Real-time 3D visualization of forest fire spread based on tree morphology and finite state machineComputers & Graphics10.1016/j.cag.2022.01.009103(109-120)Online publication date: Apr-2022
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