Vsevolod Cherepashkin, Erenus Yildiz, Andreas Fischbach, Leif Kobbelt, and Hanno Scharr, “Deep Learning Based 3d Reconstruction for Phenotyping of Wheat Seeds: a Dataset, Challenge, and Baseline Method,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Oct. 2023, pp. 561–571.
@inproceedings{cherepashkin_deep_2023,
author = {Cherepashkin, Vsevolod and Yildiz, Erenus and Fischbach, Andreas and Kobbelt, Leif and Scharr, Hanno},
title = {Deep Learning Based 3d Reconstruction for Phenotyping of Wheat Seeds: a Dataset, Challenge, and Baseline Method},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = oct,
year = {2023},
pages = {561-571},
freepdf = {https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/html/Cherepashkin_Deep_Learning_Based_3d_Reconstruction_for_Phenotyping_of_Wheat_Seeds_ICCVW_2023_paper.html},
website = {https://helmholtz-data-challenges.de/web/challenges/challenge-page/135/overview},
tldr = {Three-dimensional reconstruction of wheat seeds for phenotyping. The most relevant trait is seed volume, because it is indicative of seed mass, which correlates to nutrients available to a seedling plant. The dataset consists of image data from robotic system phenoSeeder; different proportions of data are used in different scenarios. Test data is held back and uses three views; dataset train / val sets and challenge at the website link. Baseline methods are VGG11 and ResNet-152, no code published for the baselines.}
}
tl;dr: Three-dimensional reconstruction of wheat seeds for phenotyping. The most relevant trait is seed volume, because it is indicative of seed mass, which correlates to nutrients available to a seedling plant. The dataset consists of image data from robotic system phenoSeeder; different proportions of data are used in different scenarios. Test data is held back and uses three views; dataset train / val sets and challenge at the website link. Baseline methods are VGG11 and ResNet-152, no code published for the baselines.
Deep Learning Based 3d Reconstruction for Phenotyping of Wheat Seeds: a Dataset, Challenge, and Baseline Method.
Vsevolod Cherepashkin, Erenus Yildiz, Andreas Fischbach, Leif Kobbelt, and Hanno Scharr.
pdf: https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/html/Cherepashkin_Deep_Learning_Based_3d_Reconstruction_for_Phenotyping_of_Wheat_Seeds_ICCVW_2023_paper.html
h/t Amy Tabbweb: https://helmholtz-data-challenges.de/web/challenges/challenge-page/135/overview