Paper Info
Title
Learning to reconstruct botanical trees from single images
Abstract
AbstractWe 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.
Year
DOI
Venue
2021
10.1145/3478513.3480525
ACM Transactions on Graphics
Keywords
DocType
Volume
Botanical Tree Models, Tree Reconstruction, Bounding Volumes, Shape Reconstruction
Journal
40
Issue
ISSN
Citations 
6
0730-0301
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Bosheng Li100.34
Jacek Kaluzny200.34
Jonathan Klein3122.35
Dominik L. Michels44311.63
Wojtek Pałubicki520.70
Bedrich Benes6127680.15
Sören Pirk700.68