FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3166793909> ?p ?o ?g. }

• W3166793909 abstract "Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes. From a mesh model, point clouds are necessary along with a robust distance metric to assess surface quality or as part of the loss function for training models. Current methods often rely on a uniform random mesh discretization, which yields irregular sampling and noisy distance estimation. In this paper we introduce MongeNet, a fast and optimal transport based sampler that allows for an accurate discretization of a mesh with better approximation properties. We compare our method to the ubiquitous random uniform sampling and show that the approximation error is almost half with a very small computational overhead." @default.
• W3166793909 created "2021-06-22" @default.
• W3166793909 creator A5025220020 @default.
• W3166793909 creator A5034095159 @default.
• W3166793909 creator A5055226360 @default.
• W3166793909 creator A5082533938 @default.
• W3166793909 date "2021-06-01" @default.
• W3166793909 modified "2023-10-08" @default.
• W3166793909 title "MongeNet: Efficient Sampler for Geometric Deep Learning" @default.
• W3166793909 cites W115130976 @default.
• W3166793909 cites W1499978535 @default.
• W3166793909 cites W1549932963 @default.
• W3166793909 cites W1985506636 @default.
• W3166793909 cites W1994521023 @default.
• W3166793909 cites W2001108154 @default.
• W3166793909 cites W2002667752 @default.
• W3166793909 cites W2028852533 @default.
• W3166793909 cites W2045422729 @default.
• W3166793909 cites W2049153601 @default.
• W3166793909 cites W2051752778 @default.
• W3166793909 cites W2057886711 @default.
• W3166793909 cites W2061987443 @default.
• W3166793909 cites W2067833418 @default.
• W3166793909 cites W2093625674 @default.
• W3166793909 cites W2097124770 @default.
• W3166793909 cites W2125101937 @default.
• W3166793909 cites W2153935912 @default.
• W3166793909 cites W2158131535 @default.
• W3166793909 cites W2342070830 @default.
• W3166793909 cites W2396935036 @default.
• W3166793909 cites W2554482726 @default.
• W3166793909 cites W2560722161 @default.
• W3166793909 cites W2779856146 @default.
• W3166793909 cites W2790691948 @default.
• W3166793909 cites W2884453589 @default.
• W3166793909 cites W2913142797 @default.
• W3166793909 cites W2944579304 @default.
• W3166793909 cites W2949250376 @default.
• W3166793909 cites W2962746791 @default.
• W3166793909 cites W2962778872 @default.
• W3166793909 cites W2963781081 @default.
• W3166793909 cites W2963926543 @default.
• W3166793909 cites W2985388077 @default.
• W3166793909 cites W2997088169 @default.
• W3166793909 cites W3008606616 @default.
• W3166793909 cites W3020430839 @default.
• W3166793909 cites W3034317268 @default.
• W3166793909 cites W3034470433 @default.
• W3166793909 cites W3039256091 @default.
• W3166793909 cites W3042719542 @default.
• W3166793909 cites W3101027576 @default.
• W3166793909 cites W3104622254 @default.
• W3166793909 cites W3118898589 @default.
• W3166793909 doi "https://doi.org/10.1109/cvpr46437.2021.01639" @default.
• W3166793909 hasPublicationYear "2021" @default.
• W3166793909 type Work @default.
• W3166793909 sameAs 3166793909 @default.
• W3166793909 citedByCount "1" @default.
• W3166793909 countsByYear W31667939092023 @default.
• W3166793909 crossrefType "proceedings-article" @default.
• W3166793909 hasAuthorship W3166793909A5025220020 @default.
• W3166793909 hasAuthorship W3166793909A5034095159 @default.
• W3166793909 hasAuthorship W3166793909A5055226360 @default.
• W3166793909 hasAuthorship W3166793909A5082533938 @default.
• W3166793909 hasBestOaLocation W31667939092 @default.
• W3166793909 hasConcept C105795698 @default.
• W3166793909 hasConcept C106131492 @default.
• W3166793909 hasConcept C108583219 @default.
• W3166793909 hasConcept C111919701 @default.
• W3166793909 hasConcept C11413529 @default.
• W3166793909 hasConcept C121332964 @default.
• W3166793909 hasConcept C121684516 @default.
• W3166793909 hasConcept C126255220 @default.
• W3166793909 hasConcept C131979681 @default.
• W3166793909 hasConcept C134306372 @default.
• W3166793909 hasConcept C135628077 @default.
• W3166793909 hasConcept C140779682 @default.
• W3166793909 hasConcept C154945302 @default.
• W3166793909 hasConcept C162324750 @default.
• W3166793909 hasConcept C176217482 @default.
• W3166793909 hasConcept C181145010 @default.
• W3166793909 hasConcept C19499675 @default.
• W3166793909 hasConcept C21547014 @default.
• W3166793909 hasConcept C2779960059 @default.
• W3166793909 hasConcept C2781395549 @default.
• W3166793909 hasConcept C31487907 @default.
• W3166793909 hasConcept C31972630 @default.
• W3166793909 hasConcept C33923547 @default.
• W3166793909 hasConcept C41008148 @default.
• W3166793909 hasConcept C73000952 @default.
• W3166793909 hasConcept C97355855 @default.
• W3166793909 hasConceptScore W3166793909C105795698 @default.
• W3166793909 hasConceptScore W3166793909C106131492 @default.
• W3166793909 hasConceptScore W3166793909C108583219 @default.
• W3166793909 hasConceptScore W3166793909C111919701 @default.
• W3166793909 hasConceptScore W3166793909C11413529 @default.
• W3166793909 hasConceptScore W3166793909C121332964 @default.
• W3166793909 hasConceptScore W3166793909C121684516 @default.
• W3166793909 hasConceptScore W3166793909C126255220 @default.
• W3166793909 hasConceptScore W3166793909C131979681 @default.

• next