@inproceedings{03d6222e96414c61bb73de7b02b1c47d,
title = "3D Reconstruction of Plant Leaves for High-Throughput Phenotyping",
abstract = "Generating 3D digital representations of plants is indispensable for researchers to gain a detailed understanding of plant dynamics. Emerging high-throughput plant phenotyping techniques can capture plant point clouds that, however, often contain imperfections and make it a changeling task to generate accurate 3D reconstructions. We present an end-to-end pipeline to reconstruct surfaces from point clouds of maize and rice plants. In particular, we propose a two-step clustering approach to accurately segment the points of each individual plant component according to maize and rice properties. We further employ surface fitting and edge fitting to ensure the smoothness of resulting surfaces. Realistic visualization results are obtained through post-processing, including texturing and lighting. Our experimental study has explored the parameter space and demonstrated the effectiveness of our pipeline for high-throughput plant phenotyping.",
keywords = "3D reconstruction, high-throughput plant phenotyping, point cloud",
author = "Feiyu Zhu and Suresh Thapa and Tiao Gao and Yufeng Ge and Harkamal Walia and Hongfeng Yu",
note = "Funding Information: This research has been sponsored by the National Science Foundation through grants DBI-1556186, DBI-1564621, OIA-1736192, and IIS-1423487. The authors would like to acknowledge the staff members at the University of Nebraska-Lincoln's Greenhouse Innovation Center for their assistance in data collection. Funding Information: VI. ACKNOWLEDGMENT This research has been sponsored by the National Science Foundation through grants DBI-1556186, DBI-1564621, OIA-1736192, and IIS-1423487. The authors would like to acknowledge the staff members at the University of Nebraska-Lincoln{\textquoteright}s Greenhouse Innovation Center for their assistance in data collection. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/BigData.2018.8622428",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4285--4293",
editor = "Naoki Abe and Huan Liu and Calton Pu and Xiaohua Hu and Nesreen Ahmed and Mu Qiao and Yang Song and Donald Kossmann and Bing Liu and Kisung Lee and Jiliang Tang and Jingrui He and Jeffrey Saltz",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
}