% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@INPROCEEDINGS{Baker:1032004,
author = {Baker, Dirk Norbert and Selzner, Tobias and Göbbert, Jens
Henrik and Scharr, Hanno and Riedel, Morris and Hvannberg,
Ebba Þóra and Schnepf, Andrea and Zielasko, Daniel},
title = {{H}ands-{O}n {P}lant {R}oot {S}ystem {R}econstruction in
{V}irtual {R}eality},
publisher = {ACM New York, NY, USA},
reportid = {FZJ-2024-05920},
pages = {1-2},
year = {2024},
comment = {30th ACM Symposium on Virtual Reality Software and
Technology : [Proceedings] - ACM New York, NY, USA, 2024. -
ISBN 9798400705359 - doi:10.1145/3641825.3689494},
booktitle = {30th ACM Symposium on Virtual Reality
Software and Technology : [Proceedings]
- ACM New York, NY, USA, 2024. - ISBN
9798400705359 -
doi:10.1145/3641825.3689494},
abstract = {VRoot is an immersive extended reality reconstruction tool
for root system architectures from 3D volumetric scans of
soil columns. We have conducted a laboratory user study to
assess the performance of new users with our software in
comparison to established software. We utilize a plant model
to derive a synthetic root architecture, providing a
baseline for reconstruction. This demo showcases the
processes and techniques contributing to exact and efficient
manual root architecture reconstruction in Virtual Reality.
The extraction task typically is the sparse graph-structure
extraction from a 3D magnetic-resonance imaging (MRI) data
set. We visualize the RSA directly within the MRI and offer
selection-set-based methods of adapting and augmenting the
root architecture. This application is in productive use at
our partner institute, where it is used to analyze complex
root images.},
month = {Oct},
date = {2024-10-09},
organization = {VRST '24: 30th ACM Symposium on
Virtual Reality Software and
Technology, Trier Germany (Germany), 9
Oct 2024 - 11 Oct 2024},
cin = {JSC / IBG-3 / IAS-8},
cid = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IBG-3-20101118 /
I:(DE-Juel1)IAS-8-20210421},
pnm = {2A3 - Remote Sensing (CARF - CCA) (POF4-2A3) / 5112 -
Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and
Research Groups (POF4-511) / 5121 - Supercomputing $\&$ Big
Data Facilities (POF4-512) / 2173 - Agro-biogeosystems:
controls, feedbacks and impact (POF4-217) / EUROCC-2
(DEA02266) / DFG project G:(GEPRIS)390732324 - EXC 2070:
PhenoRob - Robotik und Phänotypisierung für Nachhaltige
Nutzpflanzenproduktion (390732324)},
pid = {G:(DE-HGF)POF4-2A3 / G:(DE-HGF)POF4-5112 /
G:(DE-HGF)POF4-5121 / G:(DE-HGF)POF4-2173 /
G:(DE-Juel-1)DEA02266 / G:(GEPRIS)390732324},
typ = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
UT = {WOS:001336540500090},
doi = {10.1145/3641825.3689494},
url = {https://juser.fz-juelich.de/record/1032004},
}