A computer algorithm for reconstructing a scene from two projections View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1981-09

AUTHORS

H. C. Longuet-Higgins

ABSTRACT

A simple algorithm for computing the three-dimensional structure of a scene from a correlated pair of perspective projections is described here, when the spatial relationship between the two projections is unknown. This problem is relevant not only to photographic surveying1 but also to binocular vision2, where the non-visual information available to the observer about the orientation and focal length of each eye is much less accurate than the optical information supplied by the retinal images themselves. The problem also arises in monocular perception of motion3, where the two projections represent views which are separated in time as well as space. As Marr and Poggio4 have noted, the fusing of two images to produce a three-dimensional percept involves two distinct processes: the establishment of a 1:1 correspondence between image points in the two views—the ‘correspondence problem’—and the use of the associated disparities for determining the distances of visible elements in the scene. I shall assume that the correspondence problem has been solved; the problem of reconstructing the scene then reduces to that of finding the relative orientation of the two viewpoints. More... »

PAGES

133-135

Journal

TITLE

Nature

ISSUE

5828

VOLUME

293

Related Patents

  • Locally And Globally Locating Actors By Digital Cameras And Machine Learning
  • Associating Items With Actors Based On Digital Imagery
  • Non-Invasive Turbulent Blood Flow Imaging System
  • Super-Resolution Device, Super-Resolution Method, Super-Resolution Program, And Super-Resolution System
  • Structured Light Matching Of A Set Of Curves From Two Cameras
  • Generating Tracklets From Digital Imagery
  • Determining Model Parameters Based On Transforming A Model Of An Object
  • Determining Model Parameters Based On Transforming A Model Of An Object
  • Methods, Systems And Computer Program Products For Photogrammetric Sensor Position Estimation
  • Light Source Estimation Device That Captures Light Source Images When It Is Determined That The Imaging Device Is Not Being Used By The Cameraman
  • Methods, Systems And Computer Program Products For Photogrammetric Sensor Position Estimation
  • Method And Apparatus For Calibrating Projector-Camera System
  • Method Of Determining Physical Parameters Of Bodily Structures
  • Associating Events With Actors Based On Digital Imagery
  • Mobile Unit Motion Calculating Method, Apparatus And Navigation System
  • Locally And Globally Locating Actors By Digital Cameras And Machine Learning
  • Structured Light Matching Of A Set Of Curves From Three Cameras
  • Tracking Objects In Three-Dimensional Space Using Calibrated Visual Cameras And Depth Cameras
  • 3d Human Interface Apparatus Using Motion Recognition Based On Dynamic Image Processing
  • System, Method, And Apparatus For Generating A Three-Dimensional Representation From One Or More Two-Dimensional Images
  • Method For Determination Of 3-D Structure In Biplane Angiography
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/293133a0

    DOI

    http://dx.doi.org/10.1038/293133a0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039346861


    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": "Laboratory of Experimental Psychology, University of Sussex, BN1 9QG, Brighton, UK", 
              "id": "http://www.grid.ac/institutes/grid.12082.39", 
              "name": [
                "Laboratory of Experimental Psychology, University of Sussex, BN1 9QG, Brighton, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Longuet-Higgins", 
            "givenName": "H. C.", 
            "id": "sg:person.016150615201.12", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016150615201.12"
            ], 
            "type": "Person"
          }
        ], 
        "datePublished": "1981-09", 
        "datePublishedReg": "1981-09-01", 
        "description": "A simple algorithm for computing the three-dimensional structure of a scene from a correlated pair of perspective projections is described here, when the spatial relationship between the two projections is unknown. This problem is relevant not only to photographic surveying1 but also to binocular vision2, where the non-visual information available to the observer about the orientation and focal length of each eye is much less accurate than the optical information supplied by the retinal images themselves. The problem also arises in monocular perception of motion3, where the two projections represent views which are separated in time as well as space. As Marr and Poggio4 have noted, the fusing of two images to produce a three-dimensional percept involves two distinct processes: the establishment of a 1:1 correspondence between image points in the two views\u2014the \u2018correspondence problem\u2019\u2014and the use of the associated disparities for determining the distances of visible elements in the scene. I shall assume that the correspondence problem has been solved; the problem of reconstructing the scene then reduces to that of finding the relative orientation of the two viewpoints.", 
        "genre": "article", 
        "id": "sg:pub.10.1038/293133a0", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1018957", 
            "issn": [
              "0028-0836", 
              "1476-4687"
            ], 
            "name": "Nature", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "5828", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "293"
          }
        ], 
        "keywords": [
          "correspondence problem", 
          "non-visual information", 
          "image points", 
          "perspective projection", 
          "scene", 
          "computer algorithm", 
          "retinal images", 
          "algorithm", 
          "simple algorithm", 
          "spatial relationships", 
          "monocular perception", 
          "images", 
          "information", 
          "visible elements", 
          "focal length", 
          "fusing", 
          "optical information", 
          "projections", 
          "view", 
          "correlated pair", 
          "viewpoint", 
          "space", 
          "correspondence", 
          "Marr", 
          "three-dimensional percept", 
          "process", 
          "point", 
          "time", 
          "distance", 
          "use", 
          "elements", 
          "relative orientation", 
          "pairs", 
          "orientation", 
          "observer", 
          "perception", 
          "structure", 
          "percept", 
          "three-dimensional structure", 
          "establishment", 
          "eyes", 
          "relationship", 
          "disparities", 
          "length", 
          "distinct processes", 
          "problem"
        ], 
        "name": "A computer algorithm for reconstructing a scene from two projections", 
        "pagination": "133-135", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1039346861"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/293133a0"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/293133a0", 
          "https://app.dimensions.ai/details/publication/pub.1039346861"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:27", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_157.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1038/293133a0"
      }
    ]
     

    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.10.1038/293133a0'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/293133a0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/293133a0'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/293133a0'


     

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

    103 TRIPLES      20 PREDICATES      71 URIs      63 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/293133a0 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N20f6171b61fe4d9da7e5f268b05f32e7
    4 schema:datePublished 1981-09
    5 schema:datePublishedReg 1981-09-01
    6 schema:description A simple algorithm for computing the three-dimensional structure of a scene from a correlated pair of perspective projections is described here, when the spatial relationship between the two projections is unknown. This problem is relevant not only to photographic surveying1 but also to binocular vision2, where the non-visual information available to the observer about the orientation and focal length of each eye is much less accurate than the optical information supplied by the retinal images themselves. The problem also arises in monocular perception of motion3, where the two projections represent views which are separated in time as well as space. As Marr and Poggio4 have noted, the fusing of two images to produce a three-dimensional percept involves two distinct processes: the establishment of a 1:1 correspondence between image points in the two views—the ‘correspondence problem’—and the use of the associated disparities for determining the distances of visible elements in the scene. I shall assume that the correspondence problem has been solved; the problem of reconstructing the scene then reduces to that of finding the relative orientation of the two viewpoints.
    7 schema:genre article
    8 schema:isAccessibleForFree false
    9 schema:isPartOf N275e5c98aa994b6197923c3f3e31de21
    10 N38050df2f6a74cdd843c1e05247ecdf8
    11 sg:journal.1018957
    12 schema:keywords Marr
    13 algorithm
    14 computer algorithm
    15 correlated pair
    16 correspondence
    17 correspondence problem
    18 disparities
    19 distance
    20 distinct processes
    21 elements
    22 establishment
    23 eyes
    24 focal length
    25 fusing
    26 image points
    27 images
    28 information
    29 length
    30 monocular perception
    31 non-visual information
    32 observer
    33 optical information
    34 orientation
    35 pairs
    36 percept
    37 perception
    38 perspective projection
    39 point
    40 problem
    41 process
    42 projections
    43 relationship
    44 relative orientation
    45 retinal images
    46 scene
    47 simple algorithm
    48 space
    49 spatial relationships
    50 structure
    51 three-dimensional percept
    52 three-dimensional structure
    53 time
    54 use
    55 view
    56 viewpoint
    57 visible elements
    58 schema:name A computer algorithm for reconstructing a scene from two projections
    59 schema:pagination 133-135
    60 schema:productId N28eee1a5eb594f3a93084d8eb8a1e835
    61 N9bfad41bd7f54da1bc2d139a33c6f8e1
    62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039346861
    63 https://doi.org/10.1038/293133a0
    64 schema:sdDatePublished 2022-10-01T06:27
    65 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    66 schema:sdPublisher Nb5d5348213a8489582453e4868e2e71f
    67 schema:url https://doi.org/10.1038/293133a0
    68 sgo:license sg:explorer/license/
    69 sgo:sdDataset articles
    70 rdf:type schema:ScholarlyArticle
    71 N20f6171b61fe4d9da7e5f268b05f32e7 rdf:first sg:person.016150615201.12
    72 rdf:rest rdf:nil
    73 N275e5c98aa994b6197923c3f3e31de21 schema:volumeNumber 293
    74 rdf:type schema:PublicationVolume
    75 N28eee1a5eb594f3a93084d8eb8a1e835 schema:name dimensions_id
    76 schema:value pub.1039346861
    77 rdf:type schema:PropertyValue
    78 N38050df2f6a74cdd843c1e05247ecdf8 schema:issueNumber 5828
    79 rdf:type schema:PublicationIssue
    80 N9bfad41bd7f54da1bc2d139a33c6f8e1 schema:name doi
    81 schema:value 10.1038/293133a0
    82 rdf:type schema:PropertyValue
    83 Nb5d5348213a8489582453e4868e2e71f schema:name Springer Nature - SN SciGraph project
    84 rdf:type schema:Organization
    85 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    86 schema:name Information and Computing Sciences
    87 rdf:type schema:DefinedTerm
    88 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    89 schema:name Artificial Intelligence and Image Processing
    90 rdf:type schema:DefinedTerm
    91 sg:journal.1018957 schema:issn 0028-0836
    92 1476-4687
    93 schema:name Nature
    94 schema:publisher Springer Nature
    95 rdf:type schema:Periodical
    96 sg:person.016150615201.12 schema:affiliation grid-institutes:grid.12082.39
    97 schema:familyName Longuet-Higgins
    98 schema:givenName H. C.
    99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016150615201.12
    100 rdf:type schema:Person
    101 grid-institutes:grid.12082.39 schema:alternateName Laboratory of Experimental Psychology, University of Sussex, BN1 9QG, Brighton, UK
    102 schema:name Laboratory of Experimental Psychology, University of Sussex, BN1 9QG, Brighton, UK
    103 rdf:type schema:Organization
     




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


    ...