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000902616 0247_ $$2arXiv$$aarXiv:2110.14946
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000902616 037__ $$aFZJ-2021-04411
000902616 041__ $$aEnglish
000902616 1001_ $$0P:(DE-Juel1)185995$$aHelmrich, Dirk Norbert$$b0$$eCorresponding author$$ufzj
000902616 1112_ $$a11th IEEE Symposium on Large Data Analysis and Visualization$$cVirtual$$d2021-10-25 - 2021-10-25$$gLDAV2021$$wUSA
000902616 245__ $$aTowards Large-Scale Rendering of Simulated Crops for Synthetic Ground Truth Generation on Modular Supercomputers
000902616 260__ $$c2021
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000902616 520__ $$aComputer Vision problems deal with the semantic extraction of information from camera images. Especially for field crop images, the underlying problems are hard to label and even harder to learn, and the availability of high-quality training data is low. Deep neural networks do a good job of extracting the necessary models from training examples. However, they rely on an abundance of training data that is not feasible to generate or label by expert annotation. To address this challenge, we make use of the Unreal Engine to render large and complex virtual scenes. We rely on the performance of individual nodes by distributing plant simulations across nodes and both generate scenes as well as train neural networks on GPUs, restricting node communication to parallel learning.
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000902616 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x1
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000902616 7001_ $$0P:(DE-Juel1)168541$$aGöbbert, Jens Henrik$$b1$$ufzj
000902616 7001_ $$0P:(DE-Juel1)180766$$aGiraud, Mona$$b2$$ufzj
000902616 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b3$$ufzj
000902616 7001_ $$0P:(DE-Juel1)157922$$aSchnepf, Andrea$$b4$$ufzj
000902616 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b5$$ufzj
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