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| 001 | 902616 | ||
| 005 | 20211128011508.0 | ||
| 024 | 7 | _ | |a arXiv:2110.14946 |2 arXiv |
| 024 | 7 | _ | |a 2128/29111 |2 Handle |
| 024 | 7 | _ | |a altmetric:115929758 |2 altmetric |
| 037 | _ | _ | |a FZJ-2021-04411 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Helmrich, Dirk Norbert |0 P:(DE-Juel1)185995 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 11th IEEE Symposium on Large Data Analysis and Visualization |g LDAV2021 |c Virtual |d 2021-10-25 - 2021-10-25 |w USA |
| 245 | _ | _ | |a Towards Large-Scale Rendering of Simulated Crops for Synthetic Ground Truth Generation on Modular Supercomputers |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Computer Vision problems deal with the semantic extraction of information from camera images. Especially for field crop images, the underlying problems are hard to label and even harder to learn, and the availability of high-quality training data is low. Deep neural networks do a good job of extracting the necessary models from training examples. However, they rely on an abundance of training data that is not feasible to generate or label by expert annotation. To address this challenge, we make use of the Unreal Engine to render large and complex virtual scenes. We rely on the performance of individual nodes by distributing plant simulations across nodes and both generate scenes as well as train neural networks on GPUs, restricting node communication to parallel learning. |
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| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 3 |u fzj |
| 700 | 1 | _ | |a Schnepf, Andrea |0 P:(DE-Juel1)157922 |b 4 |u fzj |
| 700 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 5 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/902616/files/2-Page%20Summary.pdf |y OpenAccess |
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