Institution
Technical University of Dortmund
Education•Dortmund, 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.
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Papers
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169 citations
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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
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23 Aug 2020TL;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
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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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Hermann Kolanoski | 145 | 1279 | 96152 |
Marc Besancon | 143 | 1799 | 106869 |
Kerstin Borras | 133 | 1341 | 92173 |
Emmerich Kneringer | 129 | 1021 | 80898 |
Achim Geiser | 129 | 1331 | 84136 |
Valerio Vercesi | 129 | 937 | 79519 |
Jens Weingarten | 128 | 896 | 74667 |
Giuseppe Mornacchi | 127 | 894 | 75830 |
Kevin Kroeninger | 126 | 836 | 70010 |
Daniel Muenstermann | 126 | 885 | 70855 |
Reiner Klingenberg | 126 | 733 | 70069 |
Claus Gössling | 126 | 775 | 71975 |
Diane Cinca | 126 | 822 | 70126 |
Frank Meier | 124 | 677 | 64889 |
Daniel Dobos | 124 | 679 | 67434 |