Ontology type: schema:ScholarlyArticle
2019-02
AUTHORSPeihua Song, Youyi Zheng, Jinyuan Jia, Yan Gao
ABSTRACTFurniture layout in a virtual 3D scene is an important and challenging task, as it is time-consuming and requires experience. To address this issue, we propose automatic furniture layout algorithms to help users to rapidly generate reasonable layout results. Specifically, our algorithms divide a scene layout into four layout modes, namely, coupled mode, enclosed mode, matrix mode, and circular mode. Then each model is solved independently. The coupled mode is solved using recursive techniques and case-based reasoning. The enclosed mode is solved using floor field. The distance and angle among the furniture are determined by ergonomics guidelines. Finally, the layout results of the scene can be obtained by combining the solutions from these layout modes, and an evaluation method for the layout results based on user feedback is proposed. For a room with non-rectangular floor, our algorithms can also handle this case using shape standardization techniques. Based on our algorithms, an online 3D furniture layout system is developed. Many experiments are conducted on the system with the real interior design cases, and we compared our algorithms with other popular algorithms. The experimental results show that our algorithms are efficient and can meet the real response requirements of online furniture layout. More... »
PAGES5051-5079
http://scigraph.springernature.com/pub.10.1007/s11042-018-6334-5
DOIhttp://dx.doi.org/10.1007/s11042-018-6334-5
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1105319636
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": "Tongji University",
"id": "https://www.grid.ac/institutes/grid.24516.34",
"name": [
"School of Software Engineering, Tongji University, 201804, Shanghai, People\u2019s Republic of China"
],
"type": "Organization"
},
"familyName": "Song",
"givenName": "Peihua",
"id": "sg:person.014042526602.09",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014042526602.09"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Zhejiang University",
"id": "https://www.grid.ac/institutes/grid.13402.34",
"name": [
"State Key Lab of CAD & CG, Zhejiang University, 310058, Hangzhou, People\u2019s Republic of China"
],
"type": "Organization"
},
"familyName": "Zheng",
"givenName": "Youyi",
"id": "sg:person.011112606174.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011112606174.21"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Tongji University",
"id": "https://www.grid.ac/institutes/grid.24516.34",
"name": [
"School of Software Engineering, Tongji University, 201804, Shanghai, People\u2019s Republic of China"
],
"type": "Organization"
},
"familyName": "Jia",
"givenName": "Jinyuan",
"id": "sg:person.012204254726.04",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012204254726.04"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Clarkson University",
"id": "https://www.grid.ac/institutes/grid.254280.9",
"name": [
"Electrical and Computer Engineering, Clarkson University, 13676, Potsdam, NY, USA"
],
"type": "Organization"
},
"familyName": "Gao",
"givenName": "Yan",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s11042-013-1516-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001877471",
"https://doi.org/10.1007/s11042-013-1516-7"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/2766898",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008856189"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/2461912.2461968",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016167666"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/1185657.1185716",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016435350"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00155578",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016957726",
"https://doi.org/10.1007/bf00155578"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00155578",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016957726",
"https://doi.org/10.1007/bf00155578"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-016-4256-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019831799",
"https://doi.org/10.1007/s11042-016-4256-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-016-4256-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019831799",
"https://doi.org/10.1007/s11042-016-4256-7"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cag.2014.09.032",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030666071"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-015-2481-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043415705",
"https://doi.org/10.1007/s11042-015-2481-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-014-2041-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043951393",
"https://doi.org/10.1007/s11042-014-2041-z"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/199404.199427",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046275393"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1467-8659.2009.01351.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046936418"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1467-8659.2009.01351.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046936418"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/2629573",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053380495"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/2366145.2366154",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053492172"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreve.78.021131",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060737736"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreve.78.021131",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060737736"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/1141911.1141931",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1063151955"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/2010324.1964981",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1063159744"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/2010324.1964982",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1063159745"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-017-4457-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084028903",
"https://doi.org/10.1007/s11042-017-4457-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-017-4457-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084028903",
"https://doi.org/10.1007/s11042-017-4457-8"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1142/s0129183117500590",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084226725"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/cisis.2013.130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095735577"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/cisis.2013.130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095735577"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/1278780.1278822",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1096186822"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-02",
"datePublishedReg": "2019-02-01",
"description": "Furniture layout in a virtual 3D scene is an important and challenging task, as it is time-consuming and requires experience. To address this issue, we propose automatic furniture layout algorithms to help users to rapidly generate reasonable layout results. Specifically, our algorithms divide a scene layout into four layout modes, namely, coupled mode, enclosed mode, matrix mode, and circular mode. Then each model is solved independently. The coupled mode is solved using recursive techniques and case-based reasoning. The enclosed mode is solved using floor field. The distance and angle among the furniture are determined by ergonomics guidelines. Finally, the layout results of the scene can be obtained by combining the solutions from these layout modes, and an evaluation method for the layout results based on user feedback is proposed. For a room with non-rectangular floor, our algorithms can also handle this case using shape standardization techniques. Based on our algorithms, an online 3D furniture layout system is developed. Many experiments are conducted on the system with the real interior design cases, and we compared our algorithms with other popular algorithms. The experimental results show that our algorithms are efficient and can meet the real response requirements of online furniture layout.",
"genre": "research_article",
"id": "sg:pub.10.1007/s11042-018-6334-5",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1044869",
"issn": [
"1380-7501",
"1573-7721"
],
"name": "Multimedia Tools and Applications",
"type": "Periodical"
},
{
"issueNumber": "4",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "78"
}
],
"name": "Web3D-based automatic furniture layout system using recursive case-based reasoning and floor field",
"pagination": "5051-5079",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"6c11ce4ae85ccf2d795c647cc777f1f5fded67d7ccf354b421a55e32adc46bc7"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11042-018-6334-5"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1105319636"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11042-018-6334-5",
"https://app.dimensions.ai/details/publication/pub.1105319636"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T13:07",
"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/v90/0000000367_0000000367/records_88221_00000001.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs11042-018-6334-5"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s11042-018-6334-5'
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/s11042-018-6334-5'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11042-018-6334-5'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11042-018-6334-5'
This table displays all metadata directly associated to this object as RDF triples.
156 TRIPLES
21 PREDICATES
48 URIs
19 LITERALS
7 BLANK NODES