{"id":"https://openalex.org/W3167259153","doi":"https://doi.org/10.1109/cvpr46437.2021.01121","title":"SOLD2: Self-supervised Occlusion-aware Line Description and Detection","display_name":"SOLD2: Self-supervised Occlusion-aware Line Description and Detection","publication_year":2021,"publication_date":"2021-06-01","ids":{"openalex":"https://openalex.org/W3167259153","doi":"https://doi.org/10.1109/cvpr46437.2021.01121","mag":"3167259153"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr46437.2021.01121","pdf_url":null,"source":null,"license":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.03362","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066256182","display_name":"R\u00e9mi Pautrat","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Remi Pautrat","raw_affiliation_string":"ETH Zurich, Department of Computer Science,","raw_affiliation_strings":["ETH Zurich, Department of Computer Science,"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091891363","display_name":"Juan-Ting Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Juan-Ting Lin","raw_affiliation_string":"ETH Zurich, Department of Computer Science,","raw_affiliation_strings":["ETH Zurich, Department of Computer Science,"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056112272","display_name":"Viktor Larsson","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Viktor Larsson","raw_affiliation_string":"ETH Zurich, Department of Computer Science,","raw_affiliation_strings":["ETH Zurich, Department of Computer Science,"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040640817","display_name":"Martin R. Oswald","orcid":"https://orcid.org/0000-0002-1183-9958"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Martin R. Oswald","raw_affiliation_string":"ETH Zurich, Department of Computer Science,","raw_affiliation_strings":["ETH Zurich, Department of Computer Science,"]},{"author_position":"last","author":{"id":"https://openalex.org/A5021908609","display_name":"Marc Pollefeys","orcid":"https://orcid.org/0000-0003-2448-2318"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Marc Pollefeys","raw_affiliation_string":"Department of Computer Science, ETH Zurich; Microsoft Mixed Reality and AI Zurich lab","raw_affiliation_strings":["Department of Computer Science, ETH Zurich","Microsoft Mixed Reality and AI Zurich lab"]}],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"has_fulltext":false,"cited_by_count":36,"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Simultaneous Localization and Mapping","score":0.9999,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Simultaneous Localization and Mapping","score":0.9999,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Image Feature Retrieval and Recognition Techniques","score":0.9997,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Robust Line and Curve Detection using Hough Transform","score":0.9995,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"keyword":"line","score":0.4768},{"keyword":"detection","score":0.3976},{"keyword":"description","score":0.2552},{"keyword":"self-supervised","score":0.25},{"keyword":"occlusion-aware","score":0.25}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.80591273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7710173},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.76933753},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.63365424},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.62024945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5813474},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5506805},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5283328},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5173799},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5027845},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.48725834},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4865231},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48545414},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4620146},{"id":"https://openalex.org/C38785706","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Interest point detection","level":5,"score":0.44708803},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42422795},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3250153},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.26389962},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.20582199},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.086634725},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C188082640","wikidata":"https://www.wikidata.org/wiki/Q1780899","display_name":"Complementation","level":4,"score":0.0},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr46437.2021.01121","pdf_url":null,"source":null,"license":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2104.03362","pdf_url":"https://arxiv.org/pdf/2104.03362","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2104.03362","pdf_url":"https://arxiv.org/pdf/2104.03362","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.72}],"grants":[],"referenced_works_count":47,"referenced_works":["https://openalex.org/W1498183729","https://openalex.org/W1921947585","https://openalex.org/W1970044719","https://openalex.org/W1974880733","https://openalex.org/W2004470237","https://openalex.org/W2005531687","https://openalex.org/W2008849352","https://openalex.org/W2014710139","https://openalex.org/W2038991702","https://openalex.org/W2074231493","https://openalex.org/W2085579262","https://openalex.org/W2089373496","https://openalex.org/W2096532510","https://openalex.org/W2111003594","https://openalex.org/W2123212074","https://openalex.org/W2129653672","https://openalex.org/W2132341613","https://openalex.org/W2145267071","https://openalex.org/W2151103935","https://openalex.org/W2160072137","https://openalex.org/W2194775991","https://openalex.org/W2236419220","https://openalex.org/W2405450942","https://openalex.org/W2471962767","https://openalex.org/W2476548250","https://openalex.org/W2543234964","https://openalex.org/W2605111497","https://openalex.org/W2612112834","https://openalex.org/W2737353725","https://openalex.org/W2741885505","https://openalex.org/W2798943056","https://openalex.org/W2917141083","https://openalex.org/W2922243907","https://openalex.org/W2953809838","https://openalex.org/W2955186028","https://openalex.org/W2962705366","https://openalex.org/W2973689592","https://openalex.org/W2979458572","https://openalex.org/W2981415272","https://openalex.org/W3003913300","https://openalex.org/W3009337121","https://openalex.org/W3034411221","https://openalex.org/W3035228270","https://openalex.org/W3043075211","https://openalex.org/W3103648783","https://openalex.org/W3107016113","https://openalex.org/W3128759119"],"related_works":["https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2521627374","https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159"],"ngrams_url":"https://api.openalex.org/works/W3167259153/ngrams","abstract_inverted_index":{"Compared":[0],"to":[1,22,66,83,121,168],"feature":[2,174],"point":[3],"detection":[4,54,133],"and":[5,8,42,55,79,90,124,134,159,162,178],"description,":[6],"detecting":[7],"matching":[9,160],"line":[10,16,58,77,94,113,132],"segments":[11,59,95],"offer":[12,43],"additional":[13],"challenges.":[14],"Yet,":[15],"features":[17],"represent":[18],"a":[19,61,67,165],"promising":[20],"complement":[21],"points":[23,175],"for":[24],"multi-view":[25,139],"tasks.":[26],"Lines":[27],"are":[28,181],"indeed":[29],"well-defined":[30],"by":[31],"the":[32,51,100,105,170],"image":[33],"gradient,":[34],"frequently":[35],"appear":[36],"even":[37],"in":[38,60,96,108],"poorly":[39],"textured":[40],"areas":[41],"robust":[44,120],"structural":[45],"cues.":[46],"We":[47,126],"thus":[48,163],"hereby":[49],"introduce":[50],"first":[52,166],"joint":[53],"description":[56,135],"of":[57,93],"single":[62],"deep":[63],"network.":[64],"Thanks":[65],"self-supervised":[68],"training,":[69],"our":[70,111,128],"method":[71],"does":[72],"not":[73],"require":[74],"any":[75,84],"annotated":[76],"labels":[78],"can":[80],"therefore":[81],"generalize":[82],"dataset.":[85],"Our":[86,151],"detector":[87],"offers":[88],"repeatable":[89],"accurate":[91],"localization":[92,157],"images,":[97],"departing":[98],"from":[99],"wireframe":[101],"parsing":[102],"approach.":[103],"Leveraging":[104],"recent":[106],"progresses":[107],"descriptor":[109,114],"learning,":[110],"proposed":[112],"is":[115],"highly":[116],"discriminative,":[117],"while":[118],"remaining":[119],"viewpoint":[122,149],"changes":[123],"occlusions.":[125],"evaluate":[127],"approach":[129],"against":[130],"previous":[131],"methods":[136],"on":[137],"several":[138],"datasets":[140],"created":[141],"with":[142,172],"homographic":[143],"warps":[144],"as":[145,147],"well":[146],"real-world":[148],"changes.":[150],"full":[152],"pipeline":[153],"yields":[154],"higher":[155],"repeatability,":[156],"accuracy":[158],"metrics,":[161],"represents":[164],"step":[167],"bridge":[169],"gap":[171],"learned":[173],"methods.":[176],"Code":[177],"trained":[179],"weights":[180],"available":[182],"at":[183],"https://github.com/cvg/SOLD2.":[184]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3167259153","counts_by_year":[{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2024-03-19T04:41:13.661511","created_date":"2021-06-22"}