Authors:
J. M. Jurado
;
J. L. Cárdenas
;
C. J. Ogayar
;
L. Ortega
and
F. R. Feito
Affiliation:
Computer Graphics and Geomatics Group, University of Jaén and Spain
Keyword(s):
3D Plant Reconstruction, Spectral Reflectance, Image Processing, Procedural Modeling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Modeling and Algorithms
;
Modeling of Natural Scenes and Phenomena
;
Pattern Recognition
;
Software Engineering
;
Solid and Heterogeneous Modeling
Abstract:
In this paper, we propose a framework for accurate plant modeling constrained to actual plant-light interaction along a time-interval. To this end, several plant models have been generated by using data from different sources such as LiDAR scanning, optical cameras and multispectral sensors. In contrast to previous approaches that mostly focus on realistic rendering purposes, the main objective of our method is to improve the multiview stereo reconstruction of plant structures and the prediction of the growth of existing plants according to the influence of real light incidence. Our experimental results are oriented to olive trees, which are formed by many thin branches and dense foliage. Plant reconstruction is a challenging task due to self-occlusion. Our approach is based on inverse modeling to generate a parametric model which describes how plants evolve in a time interval by considering the surrounding environment. A multispectral sensor has been used to characterize input plant
models from reflectance values for each narrow-band. We propose the fusion of heterogeneous data to achieve a more accurate modeling of plant structure and the prediction of the branching fate.
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