Two accelerating techniques for 3D reconstruction View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-05

AUTHORS

Shixia Liu, Shimin Hu, Jiaguang Sun

ABSTRACT

Automatic reconstruction of 3D objects from 2D orthographic views has been a major research issue in CAD/CAM. In this paper, two accelerating techniques to improve the efficiency of reconstruction are presented. First, some pseudo elements are removed by depth and topology information as soon as the wire-frame is constructed, which reduces the searching space. Second, the proposed algorithm does not establish all possible surfaces in the process of generating 3D faces. The surfaces and edge loops are generated by using the relationship between the boundaries of 3D faces and their projections. This avoids the growth in combinational complexity of previous methods that have to check all possible pairs of 3D candidate edges. More... »

PAGES

362-368

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02947315

DOI

http://dx.doi.org/10.1007/bf02947315

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021748276


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Shixia", 
        "id": "sg:person.01326314771.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326314771.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hu", 
        "givenName": "Shimin", 
        "id": "sg:person.013413402617.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013413402617.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Jiaguang", 
        "id": "sg:person.011411464635.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011411464635.59"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2002-05", 
    "datePublishedReg": "2002-05-01", 
    "description": "Automatic reconstruction of 3D objects from 2D orthographic views has been a major research issue in CAD/CAM. In this paper, two accelerating techniques to improve the efficiency of reconstruction are presented. First, some pseudo elements are removed by depth and topology information as soon as the wire-frame is constructed, which reduces the searching space. Second, the proposed algorithm does not establish all possible surfaces in the process of generating 3D faces. The surfaces and edge loops are generated by using the relationship between the boundaries of 3D faces and their projections. This avoids the growth in combinational complexity of previous methods that have to check all possible pairs of 3D candidate edges.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/bf02947315", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1357568", 
        "issn": [
          "1000-9000", 
          "1860-4749"
        ], 
        "name": "Journal of Computer Science and Technology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "17"
      }
    ], 
    "keywords": [
      "major research issues", 
      "topology information", 
      "automatic reconstruction", 
      "research issues", 
      "efficiency of reconstruction", 
      "CAD/CAM", 
      "candidate edges", 
      "orthographic views", 
      "previous methods", 
      "combinational complexity", 
      "edge loops", 
      "pseudo elements", 
      "algorithm", 
      "complexity", 
      "objects", 
      "reconstruction", 
      "possible pairs", 
      "possible surface", 
      "technique", 
      "information", 
      "surface", 
      "space", 
      "issues", 
      "efficiency", 
      "face", 
      "edge", 
      "projections", 
      "method", 
      "view", 
      "boundaries", 
      "depth", 
      "process", 
      "loop", 
      "elements", 
      "CAM", 
      "pairs", 
      "growth", 
      "relationship", 
      "paper"
    ], 
    "name": "Two accelerating techniques for 3D reconstruction", 
    "pagination": "362-368", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1021748276"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf02947315"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf02947315", 
      "https://app.dimensions.ai/details/publication/pub.1021748276"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:12", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_359.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/bf02947315"
  }
]
 

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/bf02947315'

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/bf02947315'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02947315'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02947315'


 

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

111 TRIPLES      21 PREDICATES      65 URIs      57 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf02947315 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N63028c3ad5574f25bf25e60124a68545
4 schema:datePublished 2002-05
5 schema:datePublishedReg 2002-05-01
6 schema:description Automatic reconstruction of 3D objects from 2D orthographic views has been a major research issue in CAD/CAM. In this paper, two accelerating techniques to improve the efficiency of reconstruction are presented. First, some pseudo elements are removed by depth and topology information as soon as the wire-frame is constructed, which reduces the searching space. Second, the proposed algorithm does not establish all possible surfaces in the process of generating 3D faces. The surfaces and edge loops are generated by using the relationship between the boundaries of 3D faces and their projections. This avoids the growth in combinational complexity of previous methods that have to check all possible pairs of 3D candidate edges.
7 schema:genre article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N264a8ef964f441de9115a97ff4bb3884
11 Nabf908126c6e4903a4fea24feb1378b4
12 sg:journal.1357568
13 schema:keywords CAD/CAM
14 CAM
15 algorithm
16 automatic reconstruction
17 boundaries
18 candidate edges
19 combinational complexity
20 complexity
21 depth
22 edge
23 edge loops
24 efficiency
25 efficiency of reconstruction
26 elements
27 face
28 growth
29 information
30 issues
31 loop
32 major research issues
33 method
34 objects
35 orthographic views
36 pairs
37 paper
38 possible pairs
39 possible surface
40 previous methods
41 process
42 projections
43 pseudo elements
44 reconstruction
45 relationship
46 research issues
47 space
48 surface
49 technique
50 topology information
51 view
52 schema:name Two accelerating techniques for 3D reconstruction
53 schema:pagination 362-368
54 schema:productId N12587b69e1834316b6a5b0052a29f933
55 Nc917d5a951e345b7b2e9f24305ff4092
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021748276
57 https://doi.org/10.1007/bf02947315
58 schema:sdDatePublished 2022-01-01T18:12
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher Nf5814ba63d554f87a18a6c4f1a923fdb
61 schema:url https://doi.org/10.1007/bf02947315
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N12587b69e1834316b6a5b0052a29f933 schema:name dimensions_id
66 schema:value pub.1021748276
67 rdf:type schema:PropertyValue
68 N264a8ef964f441de9115a97ff4bb3884 schema:issueNumber 3
69 rdf:type schema:PublicationIssue
70 N63028c3ad5574f25bf25e60124a68545 rdf:first sg:person.01326314771.26
71 rdf:rest Nfe805a351ec54ed4a5244c63d98719e4
72 Na2b5102a6f0e42c3ac0368cfef1285a5 rdf:first sg:person.011411464635.59
73 rdf:rest rdf:nil
74 Nabf908126c6e4903a4fea24feb1378b4 schema:volumeNumber 17
75 rdf:type schema:PublicationVolume
76 Nc917d5a951e345b7b2e9f24305ff4092 schema:name doi
77 schema:value 10.1007/bf02947315
78 rdf:type schema:PropertyValue
79 Nf5814ba63d554f87a18a6c4f1a923fdb schema:name Springer Nature - SN SciGraph project
80 rdf:type schema:Organization
81 Nfe805a351ec54ed4a5244c63d98719e4 rdf:first sg:person.013413402617.49
82 rdf:rest Na2b5102a6f0e42c3ac0368cfef1285a5
83 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
84 schema:name Information and Computing Sciences
85 rdf:type schema:DefinedTerm
86 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
87 schema:name Artificial Intelligence and Image Processing
88 rdf:type schema:DefinedTerm
89 sg:journal.1357568 schema:issn 1000-9000
90 1860-4749
91 schema:name Journal of Computer Science and Technology
92 schema:publisher Springer Nature
93 rdf:type schema:Periodical
94 sg:person.011411464635.59 schema:affiliation grid-institutes:grid.12527.33
95 schema:familyName Sun
96 schema:givenName Jiaguang
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011411464635.59
98 rdf:type schema:Person
99 sg:person.01326314771.26 schema:affiliation grid-institutes:grid.12527.33
100 schema:familyName Liu
101 schema:givenName Shixia
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326314771.26
103 rdf:type schema:Person
104 sg:person.013413402617.49 schema:affiliation grid-institutes:grid.12527.33
105 schema:familyName Hu
106 schema:givenName Shimin
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013413402617.49
108 rdf:type schema:Person
109 grid-institutes:grid.12527.33 schema:alternateName Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China
110 schema:name Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, P.R. China
111 rdf:type schema:Organization
 




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


...