Accurate Plant Modeling based on the Real Light Incidence

J. M. Jurado, J. L. Cárdenas, C. J. Ogayar, L. Ortega, F. R. Feito

2019

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.

Paper Citation

in Harvard Style

Jurado J., Cárdenas J., Ogayar C., Ortega L. and Feito F. (2019). Accurate Plant Modeling based on the Real Light Incidence. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP; ISBN 978-989-758-354-4, SciTePress, pages 360-366. DOI: 10.5220/0007686803600366

in Bibtex Style

@conference{grapp19,
author={J. M. Jurado and J. L. Cárdenas and C. J. Ogayar and L. Ortega and F. R. Feito},
title={Accurate Plant Modeling based on the Real Light Incidence},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP},
year={2019},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007686803600366},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP
TI - Accurate Plant Modeling based on the Real Light Incidence
SN - 978-989-758-354-4
AU - Jurado J.
AU - Cárdenas J.
AU - Ogayar C.
AU - Ortega L.
AU - Feito F.
PY - 2019
SP - 360
EP - 366
DO - 10.5220/0007686803600366
PB - SciTePress