File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform

TitleSEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
Authors
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://www.computer.org/tvcg
Citation
IEEE Transactions on Visualization and Computer Graphics, 2020, p. 1-1 How to Cite?
AbstractSegmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Existing methods for 3D shape segmentation suffer from complex geometry processing and heavy computation caused by using low-level features and fragmented segmentation results due to the lack of global consideration. We present an efficient method, called SEG-MAT, based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able to develop a simple and principled approach to effectively identify the various types of junctions between different parts of a 3D shape. Extensive evaluations and comparisons show that our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster
Persistent Identifierhttp://hdl.handle.net/10722/293191

 

DC FieldValueLanguage
dc.contributor.authorLIN, C-
dc.contributor.authorLIU, L-
dc.contributor.authorLI, C-
dc.contributor.authorKobbelt, L-
dc.contributor.authorWang, B-
dc.contributor.authorXin, S-
dc.contributor.authorWang, WP-
dc.date.accessioned2020-11-23T08:13:08Z-
dc.date.available2020-11-23T08:13:08Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Visualization and Computer Graphics, 2020, p. 1-1-
dc.identifier.urihttp://hdl.handle.net/10722/293191-
dc.description.abstractSegmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Existing methods for 3D shape segmentation suffer from complex geometry processing and heavy computation caused by using low-level features and fragmented segmentation results due to the lack of global consideration. We present an efficient method, called SEG-MAT, based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able to develop a simple and principled approach to effectively identify the various types of junctions between different parts of a 3D shape. Extensive evaluations and comparisons show that our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://www.computer.org/tvcg-
dc.relation.ispartofIEEE Transactions on Visualization and Computer Graphics-
dc.rightsIEEE Transactions on Visualization and Computer Graphics. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleSEG-MAT: 3D Shape Segmentation Using Medial Axis Transform-
dc.typeArticle-
dc.identifier.emailWang, WP: wenping@cs.hku.hk-
dc.identifier.authorityWang, WP=rp00186-
dc.identifier.doi10.1109/TVCG.2020.3032566-
dc.identifier.hkuros318866-
dc.identifier.spage1-
dc.identifier.epage1-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats