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@INPROCEEDINGS{Helmrich:902616,
      author       = {Helmrich, Dirk Norbert and Göbbert, Jens Henrik and
                      Giraud, Mona and Scharr, Hanno and Schnepf, Andrea and
                      Riedel, Morris},
      title        = {{T}owards {L}arge-{S}cale {R}endering of {S}imulated
                      {C}rops for {S}ynthetic {G}round {T}ruth {G}eneration on
                      {M}odular {S}upercomputers},
      reportid     = {FZJ-2021-04411},
      year         = {2021},
      abstract     = {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.},
      month         = {Oct},
      date          = {2021-10-25},
      organization  = {11th IEEE Symposium on Large Data
                       Analysis and Visualization, Virtual
                       (USA), 25 Oct 2021 - 25 Oct 2021},
      subtyp        = {After Call},
      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          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 2173 - Agro-biogeosystems:
                      controls, feedbacks and impact (POF4-217)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)24},
      eprint       = {2110.14946},
      howpublished = {arXiv:2110.14946},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2110.14946;\%\%$},
      url          = {https://juser.fz-juelich.de/record/902616},
}