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Article: Wireframe Parsing with Guidance of Distance Map

TitleWireframe Parsing with Guidance of Distance Map
Authors
KeywordsArtificial neural networks
computer vision
feature extraction
image edge detection
Issue Date2019
Citation
IEEE Access, 2019, v. 7, p. 141036-141044 How to Cite?
AbstractWe propose an end-To-end method for simultaneously detecting local junctions and global wireframe in man-made environment. Our pipeline consists of an anchor-free junction detection module, a distance map learning module, and a line segment proposing and verification module. A set of line segments are proposed from the predicted junctions with guidance of the learned distance map, and further verified by the proposal verification module. Experimental results show that our method outperforms the previous state-of-The-Art wireframe parser by a descent margin. In terms of line segments detection, our method shows competitive performance on standard benchmarks. The proposed networks are end-To-end trainable and efficient.aaThe code will be released on github for reproduction of the results.
Persistent Identifierhttp://hdl.handle.net/10722/345104

 

DC FieldValueLanguage
dc.contributor.authorHuang, Kun-
dc.contributor.authorGao, Shenghua-
dc.date.accessioned2024-08-15T09:25:17Z-
dc.date.available2024-08-15T09:25:17Z-
dc.date.issued2019-
dc.identifier.citationIEEE Access, 2019, v. 7, p. 141036-141044-
dc.identifier.urihttp://hdl.handle.net/10722/345104-
dc.description.abstractWe propose an end-To-end method for simultaneously detecting local junctions and global wireframe in man-made environment. Our pipeline consists of an anchor-free junction detection module, a distance map learning module, and a line segment proposing and verification module. A set of line segments are proposed from the predicted junctions with guidance of the learned distance map, and further verified by the proposal verification module. Experimental results show that our method outperforms the previous state-of-The-Art wireframe parser by a descent margin. In terms of line segments detection, our method shows competitive performance on standard benchmarks. The proposed networks are end-To-end trainable and efficient.aaThe code will be released on github for reproduction of the results.-
dc.languageeng-
dc.relation.ispartofIEEE Access-
dc.subjectArtificial neural networks-
dc.subjectcomputer vision-
dc.subjectfeature extraction-
dc.subjectimage edge detection-
dc.titleWireframe Parsing with Guidance of Distance Map-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ACCESS.2019.2943885-
dc.identifier.scopuseid_2-s2.0-85077758631-
dc.identifier.volume7-
dc.identifier.spage141036-
dc.identifier.epage141044-
dc.identifier.eissn2169-3536-

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