More Web Proxy on the site http://driver.im/

research-article

DeepTree: Modeling Trees With Situated Latents

Authors:

Sören PirkAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 30, Issue 8

Pages 5795 - 5809

https://doi.org/10.1109/TVCG.2023.3307887

Published: 01 August 2024 Publication History

Abstract

In this article, we propose <italic>DeepTree</italic>, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model “situated latent” because its behavior is determined by the intrinsic state -encoded as a latent space of a deep neural model- and by the extrinsic (environmental) data that is “situated” as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.

References

[1]

W. Palubicki et al., “Self-organizing tree models for image synthesis,” ACM Trans. Graph., vol. 28, no. 3, pp. 58:1–58:10, 2009.

[2]

B. Li et al., “Learning to reconstruct botanical trees from single images,” ACM Trans. Graph., vol. 40, no. 6, pp. 1–15, 2021.

Digital Library

[3]

Y. Livny et al., “Texture-lobes for tree modelling,” ACM Trans. Graph., vol. 30, no. 4, pp. 53:1–53:10, 2011.

Digital Library

[4]

S. Du, R. Lindenbergh, H. Ledoux, J. Stoter, and L. Nan, “AdTree: Accurate, detailed, and automatic modelling of laser-scanned trees,” Remote Sens., vol. 11, no. 18, 2019, Art. no.

[5]

O. Stava et al., “Inverse procedural modelling of trees,” Comput. Graph. Forum, vol. 33, no. 6, pp. 118–131, 2014.

Digital Library

[6]

S. Longay, A. Runions, F. Boudon, and P. Prusinkiewicz, “TreeSketch: Interactive procedural modeling of trees on a tablet,” in Proc. Int. Symp. SBIM, 2012, pp. 107–120.

[7]

Y. Liu, J. Guo, B. Benes, O. Deussen, X. Zhang, and H. Huang, “TreePartNet: Neural decomposition of point clouds for 3D tree reconstruction,” ACM Trans. Graph., vol. 40, no. 6, pp. 1–16, 2021.

Digital Library

[8]

S. Pirk et al., “Modeling plant life in computer graphics,” in Proc. ACM SIGGRAPH Courses, New York, NY, USA, 2016, pp. 18:1–18: 180.

[9]

M. Aono and T. Kunii, “Botanical tree image generation,” IEEE Comput. Graph. Appl., vol. 4, no. 5, pp. 10–34, May 1984.

Digital Library

[10]

P. E. Oppenheimer, “Real time design and animation of fractal plants and trees,” ACM SIGGRAPH Comput. Graph., vol. 20, no. 4, pp. 55–64, 1986.

Digital Library

[11]

N. Greene, “Voxel space automata: Modeling with stochastic growth processes in voxel space,” SIGGRAPH Comput. Graph., vol. 23, no. 3, pp. 175–184, 1989.

Digital Library

[12]

W. T. Reeves and R. Blau, “Approximate and probabilistic algorithms for shading and rendering structured particle systems,” SIGGRAPH Comput. Graph., vol. 19, no. 3, pp. 313–322, Jul. 1985.

Digital Library

[13]

P. Prusinkiewicz and A. Lindenmayer, The Algorithmic Beauty of Plants. Berlin, Germany: Springer, 1990.

Digital Library

[14]

R. Měch and P. Prusinkiewicz, “Visual models of plants interacting with their environment,” in Proc. 23rd Annu. Conf. Comput. Graph. Interactive Techn., 1996, pp. 397–410.

[15]

J. Weber and J. Penn, “Creation and rendering of realistic trees,” in Proc. 22nd Annu. Conf. Comput. Graph. Interactive Techn., New York, NY, USA, 1995, pp. 119–128.

[16]

B. Lintermann and O. Deussen, “Interactive modeling of plants,” IEEE Comput. Graph. Appl., vol. 19, no. 1, pp. 56–65, Jan./Feb. 1999.

Digital Library

[17]

J. O. Talton, Y. Lou, S. Lesser, J. Duke, R. Měch, and V. Koltun, “Metropolis procedural modeling,” ACM Trans. Graph., vol. 30, pp. 11:1–11:14, 2011.

[18]

J. Guo et al., “Inverse procedural modeling of branching structures by inferring l-systems,” ACM Trans. Graph., vol. 39, no. 5, pp. 1–13, Sep. 2020.

Digital Library

[19]

L. Xu and D. Mould, “Procedural tree modeling with guiding vectors,” Comput. Graph. Forum, vol. 34, no. 7, pp. 47–56, 2015.

Digital Library

[20]

V. Kohek and D. Strnad, “Interactive synthesis of self-organizing tree models on the GPU,” Computing, vol. 97, no. 2, pp. 145–169, Feb. 2015.

Digital Library

[21]

S. Pirk et al., “Plastic trees: Interactive self-adapting botanical tree models,” ACM Trans. Graph., vol. 31, no. 4, pp. 50:1–50:10, Jul. 2012.

Digital Library

[22]

B. Benes and E. U. Millán, “Virtual climbing plants competing for space,” in Proc. Comput. Animation, 2002, Art. no.

[23]

S.-K. Wong and K.-C. Chen, “A procedural approach to modelling virtual climbing plants with tendrils,” Comput. Graph. Forum, vol. 35, pp. 5–18, 2015.

[24]

S. Pirk, T. Niese, O. Deussen, and B. Neubert, “Capturing and animating the morphogenesis of polygonal tree models,” ACM Trans. Graph., vol. 31, no. 6, pp. 169:1–169:10, 2012.

Digital Library

[25]

T. Hädrich, B. Benes, O. Deussen, and S. Pirk, “Interactive modeling and authoring of climbing plants,” Comput. Graph. Forum, vol. 36, no. 2, pp. 49–61, 2017.

Digital Library

[26]

S. Pirk, T. Niese, T. Hädrich, B. Benes, and O. Deussen, “Windy trees: Computing stress response for developmental tree models,” ACM Trans. Graph., vol. 33, no. 6, pp. 204:1–204: 11, 2014.

[27]

R. Habel, A. Kusternig, and M. Wimmer, “Physically guided animation of trees,” Comput. Graph. Forum, vol. 28, no. 2, pp. 523–532, 2009.

[28]

S. Pirk, M. Jarzabek, T. Hädrich, D. L. Michels, and W. Palubicki, “Interactive wood combustion for botanical tree models,” ACM Trans. Graph., vol. 36, no. 6, pp. 197:1–197:12, Nov. 2017.

[29]

B. Wang, Y. Zhao, and J. Barbič, “Botanical materials based on biomechanics,” ACM Trans. Graph., vol. 36, no. 4, pp. 135:1–135:13, Jul. 2017.

[30]

Y. Zhao and J. Barbič, “Interactive authoring of simulation-ready plants,” ACM Trans. Graph., vol. 32, no. 4, pp. 84:1–84: 12, 2013.

[31]

B. Neubert, S. Pirk, O. Deussen, and C. Dachsbacher, “Improved model- and view-dependent pruning of large botanical scenes,” Comput. Graph. Forum, vol. 30, no. 6, pp. 1708–1718, 2011.

[32]

T. Hädrich, D. T. Banuti, W. Pałubicki, S. Pirk, and D. L. Michels, “Fire in paradise: Mesoscale simulation of wildfires,” ACM Trans. Graph., vol. 40, no. 4, pp. 1–15, Jul. 2021.

Digital Library

[33]

W. Pałubicki, M. Makowski, W. Gajda, T. Hädrich, D. L. Michels, and S. Pirk, “Ecoclimates: Climate-response modeling of vegetation,” ACM Trans. Graph., vol. 41, no. 4, pp. 1–19, 2022.

Digital Library

[34]

T. Ijiri, S. Owada, and T. Igarashi, “Seamless integration of initial sketching and subsequent detail editing in flower modeling,” Comput. Graph. Forum, vol. 25, no. 3, pp. 617–624, 2006.

[35]

M. Okabe, S. Owada, and T. Igarashi, “Interactive design of botanical trees using freehand sketches and example-based editing,” in Proc. ACM SIGGRAPH Courses, 2007, pp. 26–es.

[36]

J. Wither, F. Boudon, M.-P. Cani, and C. Godin, “Structure from silhouettes: A new paradigm for fast sketch-based design of trees,” Comput. Graph. Forum, vol. 28, no. 2, pp. 541–550, 2009.

[37]

X. Chen, B. Neubert, Y.-Q. Xu, O. Deussen, and S. B. Kang, “Sketch-based tree modeling using markov random field,” ACM Trans. Graph., vol. 27, no. 5, Dec. 2008.

[38]

D. Bradley, D. Nowrouzezahrai, and P. Beardsley, “Image-based reconstruction and synthesis of dense foliage,” ACM Trans. Graph., vol. 32, no. 4, pp. 74: 1–74:10, 2013.

[39]

P. Tan, G. Zeng, J. Wang, S. B. Kang, and L. Quan, “Image-based tree modeling,” ACM Trans. Graph., vol. 26, no. 3, pp. 87–es, 2007.

Digital Library

[40]

P. Tan, T. Fang, J. Xiao, P. Zhao, and L. Quan, “Single image tree modeling,” ACM Trans. Graph., vol. 27, no. 5, pp. 108:1–108:7, 2008.

[41]

B. Neubert, T. Franken, and O. Deussen, “Approximate image-based tree-modeling using particle flows,” ACM Trans. Graph., vol. 26, no. 3, pp. 88–es, 2007.

Digital Library

[42]

C. Li, O. Deussen, Y.-Z. Song, P. Willis, and P. Hall, “Modeling and generating moving trees from video,” ACM Trans. Graph., vol. 30, no. 6, pp. 127:1–127:12, 2011.

[43]

H. Xu, N. Gossett, and B. Chen, “Knowledge and heuristic-based modeling of laser-scanned trees,” ACM Trans. Graph., vol. 26, pp. 19–es, 2007.

Digital Library

[44]

T. Niese, S. Pirk, M. Albrecht, B. Benes, and O. Deussen, “Procedural urban forestry,” ACM Trans. Graph., vol. 41, no. 2, pp. 1–18, Mar. 2022.

Digital Library

[45]

O. Argudo, C. Andújar, and A. Chica, “Image-based tree variations,” Comput. Graph. Forum, vol. 39, no. 1, pp. 174–184, 2020.

[46]

X. Zhang et al., “Tree branch level of detail models for forest navigation,” Comput. Graph. Forum, vol. 36, no. 8, pp. 402–417, 2017.

[47]

M. Makowski, T. Hädrich, J. Scheffczyk, D. L. Michels, S. Pirk, and W. Pałubicki, “Synthetic silviculture: Multi-scale modeling of plant ecosystems,” ACM Trans. Graph., vol. 38, no. 4, pp. 1–14, Jul. 2019.

Digital Library

[48]

R. Estrada, C. Tomasi, S. C. Schmidler, and S. Farsiu, “Tree topology estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 8, pp. 1688–1701, Aug. 2015.

Digital Library

[49]

J. T. Hack, “Studies of longitudinal stream profiles in Virginia and Maryland,” US Government Printing Office, vol. 294, 1957.

[50]

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3 d classification and segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 77–85.

[51]

O. Deussen, A. Dowden-Williams, and B. Lintermann, Digital Design of Nature: Computer Generated Plants and Organics. Berlin, Germany: Springer, 2005.

[52]

S. G. Parker et al., “OptiX: A general purpose ray tracing engine,” ACM Trans. Graph., vol. 29, no. 4, pp. 1–13, Jul. 2010.

Digital Library

[53]

T. Polasek, D. Hrusa, B. Benes, and M. Cadik, “ICTree: Automatic perceptual metrics for tree models,” ACM Trans. Graph., vol. 40, no. 6, pp. 1–15, Dec. 2021.

Digital Library

[54]

J. Zhou et al., “Graph neural networks: A review of methods and applications,” AI Open, vol. 1, pp. 57–81, 2020.

[55]

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 1, pp. 4–24, Jan. 2021.

[56]

J. Xu, J. Chen, S. You, Z. Xiao, Y. Yang, and J. Lu, “Robustness of deep learning models on graphs: A survey,” AI Open, vol. 2, pp. 69–78, 2021.

[57]

H. Shao et al., “Accurately solving rod dynamics with graph learning,” in Proc. Adv. Neural Inf. Process. Syst., 2021, pp. 4829–4842.

Cited By

Zuffi SBlack M(2024)AWOL: Analysis WithOut Synthesis Using LanguageComputer Vision – ECCV 202410.1007/978-3-031-73636-0_1(1-19)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73636-0_1
Lee JLi BBenes B(2023)Latent L-systems: Transformer-based Tree GeneratorACM Transactions on Graphics10.1145/362710143:1(1-16)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3627101

Recommendations

Rank-Balanced Trees

Since the invention of AVL trees in 1962, many kinds of binary search trees have been proposed. Notable are red-black trees, in which bottom-up rebalancing after an insertion or deletion takes O(1) amortized time and O(1) rotations worst-case. But the ...
Multi-splay trees
On subtrees of trees

We study that over a certain type of trees (e.g., all trees or all binary trees) with a given number of vertices, which trees minimize or maximize the total number of subtrees (or subtrees with at least one leaf). Trees minimizing the total number of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 30, Issue 8

Aug. 2024

1479 pages

Issue’s Table of Contents

1077-2626 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 August 2024

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zuffi SBlack M(2024)AWOL: Analysis WithOut Synthesis Using LanguageComputer Vision – ECCV 202410.1007/978-3-031-73636-0_1(1-19)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73636-0_1
Lee JLi BBenes B(2023)Latent L-systems: Transformer-based Tree GeneratorACM Transactions on Graphics10.1145/362710143:1(1-16)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3627101

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents