An efficient and robust algorithm for 3D mesh segmentation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-06

AUTHORS

Lijun Chen, Nicolas D. Georganas

ABSTRACT

This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust. More... »

PAGES

109-125

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-006-0002-x

DOI

http://dx.doi.org/10.1007/s11042-006-0002-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009582905


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Ottawa", 
          "id": "https://www.grid.ac/institutes/grid.28046.38", 
          "name": [
            "School of Information Technology and Engineering, University of Ottawa, K1N 6N5, Ottawa, Ontario, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Lijun", 
        "id": "sg:person.011327715153.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011327715153.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Ottawa", 
          "id": "https://www.grid.ac/institutes/grid.28046.38", 
          "name": [
            "School of Information Technology and Engineering, University of Ottawa, K1N 6N5, Ottawa, Ontario, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Georganas", 
        "givenName": "Nicolas D.", 
        "id": "sg:person.0671247226.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0671247226.24"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1145/133994.134010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045399205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/nimg.2001.0975", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051577279"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/2945.817348", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061146321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.632982", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156702"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.632988", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mcg.1981.1673799", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061390612"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icip.2002.1038958", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093292812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/have.2002.1106913", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093333553"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2006-06", 
    "datePublishedReg": "2006-06-01", 
    "description": "This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11042-006-0002-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1044869", 
        "issn": [
          "1380-7501", 
          "1573-7721"
        ], 
        "name": "Multimedia Tools and Applications", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "29"
      }
    ], 
    "name": "An efficient and robust algorithm for 3D mesh segmentation", 
    "pagination": "109-125", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2da66ff8558ca942f061561535fb2a15fd24f8b6c081fe2adb26a48ddf6197bf"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11042-006-0002-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1009582905"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11042-006-0002-x", 
      "https://app.dimensions.ai/details/publication/pub.1009582905"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T01:59", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000510.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11042-006-0002-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11042-006-0002-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11042-006-0002-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11042-006-0002-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11042-006-0002-x'


 

This table displays all metadata directly associated to this object as RDF triples.

92 TRIPLES      21 PREDICATES      35 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11042-006-0002-x schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nbd2a84dfb85a4489aac9756e945ab96d
4 schema:citation https://doi.org/10.1006/nimg.2001.0975
5 https://doi.org/10.1109/2945.817348
6 https://doi.org/10.1109/34.632982
7 https://doi.org/10.1109/34.632988
8 https://doi.org/10.1109/have.2002.1106913
9 https://doi.org/10.1109/icip.2002.1038958
10 https://doi.org/10.1109/mcg.1981.1673799
11 https://doi.org/10.1145/133994.134010
12 schema:datePublished 2006-06
13 schema:datePublishedReg 2006-06-01
14 schema:description This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.
15 schema:genre research_article
16 schema:inLanguage en
17 schema:isAccessibleForFree false
18 schema:isPartOf N9e2635315b8c44a09fb9c224a724eaee
19 Nc73b1e2d6b9d456ab5a784a1842b8580
20 sg:journal.1044869
21 schema:name An efficient and robust algorithm for 3D mesh segmentation
22 schema:pagination 109-125
23 schema:productId N9534a0e40b2847669e02275b5a291d80
24 Na14f9dacac1243729804b2697c4cca31
25 Neb6ce126d4044cf9b2c2e869a8b4599c
26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009582905
27 https://doi.org/10.1007/s11042-006-0002-x
28 schema:sdDatePublished 2019-04-11T01:59
29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
30 schema:sdPublisher N7fc471e862dc4f5bb249318eb2a1b11c
31 schema:url http://link.springer.com/10.1007%2Fs11042-006-0002-x
32 sgo:license sg:explorer/license/
33 sgo:sdDataset articles
34 rdf:type schema:ScholarlyArticle
35 N7fc471e862dc4f5bb249318eb2a1b11c schema:name Springer Nature - SN SciGraph project
36 rdf:type schema:Organization
37 N9534a0e40b2847669e02275b5a291d80 schema:name doi
38 schema:value 10.1007/s11042-006-0002-x
39 rdf:type schema:PropertyValue
40 N9e2635315b8c44a09fb9c224a724eaee schema:issueNumber 2
41 rdf:type schema:PublicationIssue
42 Na14f9dacac1243729804b2697c4cca31 schema:name dimensions_id
43 schema:value pub.1009582905
44 rdf:type schema:PropertyValue
45 Nbd2a84dfb85a4489aac9756e945ab96d rdf:first sg:person.011327715153.42
46 rdf:rest Ne31d6abbb7bb40509e85c478005d5a04
47 Nc73b1e2d6b9d456ab5a784a1842b8580 schema:volumeNumber 29
48 rdf:type schema:PublicationVolume
49 Ne31d6abbb7bb40509e85c478005d5a04 rdf:first sg:person.0671247226.24
50 rdf:rest rdf:nil
51 Neb6ce126d4044cf9b2c2e869a8b4599c schema:name readcube_id
52 schema:value 2da66ff8558ca942f061561535fb2a15fd24f8b6c081fe2adb26a48ddf6197bf
53 rdf:type schema:PropertyValue
54 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
55 schema:name Information and Computing Sciences
56 rdf:type schema:DefinedTerm
57 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
58 schema:name Artificial Intelligence and Image Processing
59 rdf:type schema:DefinedTerm
60 sg:journal.1044869 schema:issn 1380-7501
61 1573-7721
62 schema:name Multimedia Tools and Applications
63 rdf:type schema:Periodical
64 sg:person.011327715153.42 schema:affiliation https://www.grid.ac/institutes/grid.28046.38
65 schema:familyName Chen
66 schema:givenName Lijun
67 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011327715153.42
68 rdf:type schema:Person
69 sg:person.0671247226.24 schema:affiliation https://www.grid.ac/institutes/grid.28046.38
70 schema:familyName Georganas
71 schema:givenName Nicolas D.
72 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0671247226.24
73 rdf:type schema:Person
74 https://doi.org/10.1006/nimg.2001.0975 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051577279
75 rdf:type schema:CreativeWork
76 https://doi.org/10.1109/2945.817348 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061146321
77 rdf:type schema:CreativeWork
78 https://doi.org/10.1109/34.632982 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156702
79 rdf:type schema:CreativeWork
80 https://doi.org/10.1109/34.632988 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156708
81 rdf:type schema:CreativeWork
82 https://doi.org/10.1109/have.2002.1106913 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093333553
83 rdf:type schema:CreativeWork
84 https://doi.org/10.1109/icip.2002.1038958 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093292812
85 rdf:type schema:CreativeWork
86 https://doi.org/10.1109/mcg.1981.1673799 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061390612
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1145/133994.134010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045399205
89 rdf:type schema:CreativeWork
90 https://www.grid.ac/institutes/grid.28046.38 schema:alternateName University of Ottawa
91 schema:name School of Information Technology and Engineering, University of Ottawa, K1N 6N5, Ottawa, Ontario, Canada
92 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...