Learning to reconstruct botanical trees from single images

Bosheng Li, Jacek Kałużny, Jonathan Klein, Dominik L. Michels, Wojtek Pałubicki, Bedrich Benes, Sören Pirk

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

We introduce a novel method for reconstructing the 3D geometry of botanical trees from single photographs. Faithfully reconstructing a tree from single-view sensor data is a challenging and open problem because many possible 3D trees exist that fit the tree's shape observed from a single view. We address this challenge by defining a reconstruction pipeline based on three neural networks. The networks simultaneously mask out trees in input photographs, identify a tree's species, and obtain its 3D radial bounding volume - our novel 3D representation for botanical trees. Radial bounding volumes (RBV) are used to orchestrate a procedural model primed on learned parameters to grow a tree that matches the main branching structure and the overall shape of the captured tree. While the RBV allows us to faithfully reconstruct the main branching structure, we use the procedural model's morphological constraints to generate realistic branching for the tree crown. This constraints the number of solutions of tree models for a given photograph of a tree. We show that our method reconstructs various tree species 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.
Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalACM Transactions on Graphics
Volume40
Issue number6
DOIs
StatePublished - Dec 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-12-13
Acknowledgements: This research was funded in part by National Science Foundation grant #10001387, Functional Proceduralization of 3D Geometric Models. This research was supported by the Foundation for Food and Agriculture Research Grant ID: 602757 to Benes. The content of this
publication is solely the responsibility of the authors and does not necessarily represent the official views of the foundation for Food
and Agriculture Research. Klein and Michels gratefully acknowledge the baseline funding from of the Computational Sciences Group
within KAUST’s Visual Computing Center

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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