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Institution

Technical University of Dortmund

EducationDortmund, Nordrhein-Westfalen, Germany
About: Technical University of Dortmund is a education organization based out in Dortmund, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Large Hadron Collider & Neutrino. The organization has 13028 authors who have published 27666 publications receiving 615557 citations. The organization is also known as: Dortmund University & University of Dortmund.


Papers
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Proceedings Article
01 Jan 2012
TL;DR: Cluster Layout Planarity testing Booth/Lueker and Boyer/Myrvold Cluster (Feng et al.), Upward (Bertolazzi et al.) Customizable planarization method Edge insertion (fixed & variable embedding) Crossing.
Abstract: Cluster Layout Planarity testing Booth/Lueker and Boyer/Myrvold Cluster (Feng et al) Upward (Bertolazzi et al) Customizable planarization method Edge insertion (fixed & variable embedding) Crossing Minimization optimal, minor-monotone, simultaneous Orthogonal layout Compaction (constructive + improvement) Customizable Sugiyama layout Energy-based layout (FM, ) Tree-, Circular-, Balloon-layout,

169 citations

Book ChapterDOI
23 Aug 2020
TL;DR: Deep Local Shapes (DeepLS) as discussed by the authors replaces the dense volumetric signed distance function (SDF) representation used in traditional surface reconstruction systems with a set of locally learned continuous SDFs defined by a neural network.
Abstract: Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a deep shape representation that enables encoding and reconstruction of high-quality 3D shapes without prohibitive memory requirements. DeepLS replaces the dense volumetric signed distance function (SDF) representation used in traditional surface reconstruction systems with a set of locally learned continuous SDFs defined by a neural network, inspired by recent work such as DeepSDF. Unlike DeepSDF, which represents an object-level SDF with a neural network and a single latent code, we store a grid of independent latent codes, each responsible for storing information about surfaces in a small local neighborhood. This decomposition of scenes into local shapes simplifies the prior distribution that the network must learn, and also enables efficient inference. We demonstrate the effectiveness and generalization power of DeepLS by showing object shape encoding and reconstructions of full scenes, where DeepLS delivers high compression, accuracy, and local shape completion.

169 citations

Journal ArticleDOI
TL;DR: In this article, the authors revisited the forming limit diagram (FLD) in the light of fundamental concepts of plasticity, damage and ductile fracture mechanics and proposed a new experimental methodology to determine the formability limits by fracture in sheet metal forming.

169 citations

Journal ArticleDOI
TL;DR: In this article, the authors thank the Ministry of Science and Education (MEC) (Project CTQ2007-67532-C02-01) for financial support of their research.

168 citations


Authors

Showing all 13240 results

NameH-indexPapersCitations
Hermann Kolanoski145127996152
Marc Besancon1431799106869
Kerstin Borras133134192173
Emmerich Kneringer129102180898
Achim Geiser129133184136
Valerio Vercesi12993779519
Jens Weingarten12889674667
Giuseppe Mornacchi12789475830
Kevin Kroeninger12683670010
Daniel Muenstermann12688570855
Reiner Klingenberg12673370069
Claus Gössling12677571975
Diane Cinca12682270126
Frank Meier12467764889
Daniel Dobos12467967434
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023131
2022306
20211,694
20201,773
20191,653
20181,579