{"id":"https://openalex.org/W3129090749","doi":"https://doi.org/10.1109/3dv50981.2020.00037","title":"Two-Stage Relation Constraint for Semantic Segmentation of Point Clouds","display_name":"Two-Stage Relation Constraint for Semantic Segmentation of Point Clouds","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3129090749","doi":"https://doi.org/10.1109/3dv50981.2020.00037","mag":"3129090749"},"language":"en","primary_location":{"id":"doi:10.1109/3dv50981.2020.00037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/3dv50981.2020.00037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on 3D Vision (3DV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102732753","display_name":"Minghui Yu","orcid":"https://orcid.org/0000-0003-3538-2910"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minghui Yu","raw_affiliation_strings":["Huawei Hisilicon","Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Hisilicon","institution_ids":[]},{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101470754","display_name":"Jinxian Liu","orcid":"https://orcid.org/0000-0002-4162-4528"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinxian Liu","raw_affiliation_strings":["Huawei Hisilicon","Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Hisilicon","institution_ids":[]},{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014362734","display_name":"Bingbing Ni","orcid":"https://orcid.org/0000-0001-7339-028X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingbing Ni","raw_affiliation_strings":["Huawei Hisilicon","Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Hisilicon","institution_ids":[]},{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038590946","display_name":"Caiyuan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caiyuan Li","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36756195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"271","last_page":"280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7250338196754456},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7006446123123169},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6821737289428711},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6789640188217163},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.626083493232727},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.573043167591095},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5623349547386169},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5353800058364868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5155625343322754},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4858222007751465},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4662320613861084},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.42025265097618103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.359863817691803},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3546144366264343},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3478124737739563},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.30314868688583374},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1240646243095398},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09512436389923096}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250338196754456},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7006446123123169},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6821737289428711},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6789640188217163},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.626083493232727},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.573043167591095},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5623349547386169},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5353800058364868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5155625343322754},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4858222007751465},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4662320613861084},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.42025265097618103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.359863817691803},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3546144366264343},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3478124737739563},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30314868688583374},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1240646243095398},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09512436389923096},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/3dv50981.2020.00037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/3dv50981.2020.00037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on 3D Vision (3DV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6700000166893005,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W1630959083","https://openalex.org/W1920022804","https://openalex.org/W1969198379","https://openalex.org/W2109531142","https://openalex.org/W2152322845","https://openalex.org/W2154455818","https://openalex.org/W2165874743","https://openalex.org/W2211722331","https://openalex.org/W2460657278","https://openalex.org/W2556802233","https://openalex.org/W2560609797","https://openalex.org/W2594519801","https://openalex.org/W2609719703","https://openalex.org/W2609946960","https://openalex.org/W2614059183","https://openalex.org/W2766448241","https://openalex.org/W2769312834","https://openalex.org/W2776622059","https://openalex.org/W2788158258","https://openalex.org/W2795374598","https://openalex.org/W2895472109","https://openalex.org/W2895671740","https://openalex.org/W2902302021","https://openalex.org/W2960986959","https://openalex.org/W2962928871","https://openalex.org/W2963053547","https://openalex.org/W2963057320","https://openalex.org/W2963121255","https://openalex.org/W2963182550","https://openalex.org/W2963226018","https://openalex.org/W2963231572","https://openalex.org/W2963281829","https://openalex.org/W2963312728","https://openalex.org/W2963333168","https://openalex.org/W2963509914","https://openalex.org/W2963517242","https://openalex.org/W2963719584","https://openalex.org/W2963830382","https://openalex.org/W2964266557","https://openalex.org/W2971137556","https://openalex.org/W2979750740","https://openalex.org/W2981199548","https://openalex.org/W2986237702","https://openalex.org/W2990613095","https://openalex.org/W2991084087","https://openalex.org/W2991284067","https://openalex.org/W2997216793","https://openalex.org/W2997333789","https://openalex.org/W2998719065","https://openalex.org/W3014771378","https://openalex.org/W3045094685","https://openalex.org/W3102669005","https://openalex.org/W3109051808","https://openalex.org/W4394671432","https://openalex.org/W6640300118","https://openalex.org/W6676189307","https://openalex.org/W6682494755","https://openalex.org/W6684578312","https://openalex.org/W6687484953","https://openalex.org/W6718544293","https://openalex.org/W6736894448","https://openalex.org/W6739778489","https://openalex.org/W6747904511","https://openalex.org/W6749116069","https://openalex.org/W6754849088","https://openalex.org/W6755766585","https://openalex.org/W6763422710","https://openalex.org/W6767716835","https://openalex.org/W6771775790","https://openalex.org/W6771922206","https://openalex.org/W6780658157"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2981954115"],"abstract_inverted_index":{"Key":[0],"to":[1,7,62,71,85,101,123,149],"point":[2,90],"cloud":[3,91],"semantic":[4],"segmentation":[5,186,192],"is":[6,99,121,147],"learn":[8],"discriminative":[9],"representations":[10,70],"involving":[11],"of":[12,75,176],"capturing":[13],"effective":[14],"relations":[15],"among":[16,132],"points.":[17],"Many":[18],"works":[19],"add":[20,63],"hard":[21],"constraints":[22,68],"on":[23,51,69,183],"points":[24],"through":[25],"predefined":[26],"convolution":[27],"kernels.":[28],"Motivated":[29],"by":[30,135],"label":[31],"propagation":[32],"algorithm,":[33],"we":[34,53],"develop":[35,54],"Dynamic":[36],"Adjustable":[37],"Group":[38],"Propagation":[39,59],"(DAGP)":[40],"with":[41,139],"a":[42,55,118],"dynamic":[43],"adjustable":[44],"scale":[45],"module":[46],"approximating":[47],"distance":[48],"parameter.":[49],"Based":[50],"DAGP,":[52],"novel":[56],"Two":[57],"Stage":[58],"framework":[60],"(TSP)":[61],"intra-group":[64],"and":[65,92,155,190],"intergroup":[66],"relation":[67],"enhance":[72],"the":[73,144,174],"discrimination":[74],"features":[76,87],"from":[77],"different":[78],"group":[79,106],"levels.":[80],"We":[81],"adopt":[82],"well-appreciated":[83],"backbone":[84,145],"extract":[86],"for":[88,126],"input":[89],"then":[93],"divide":[94],"them":[95],"into":[96],"groups.":[97],"DAGP":[98],"utilized":[100],"propagate":[102],"information":[103,112,153],"within":[104,154],"each":[105],"in":[107],"first":[108],"stage.":[109],"To":[110],"promote":[111],"dissemination":[113],"between":[114,156],"groups":[115,157],"more":[116],"efficiently,":[117],"selection":[119],"strategy":[120],"introduced":[122],"select":[124],"group-pairs":[125,134],"second":[127],"stage":[128],"which":[129],"propagating":[130],"labels":[131],"selected":[133],"DAGP.":[136],"By":[137],"training":[138],"this":[140],"new":[141],"learning":[142],"architecture,":[143],"network":[146],"enforced":[148],"mine":[150],"relational":[151],"context":[152],"without":[158],"introducing":[159],"any":[160],"extra":[161],"computation":[162],"burden":[163],"during":[164],"inference.":[165],"Extensive":[166],"experimental":[167],"results":[168],"show":[169],"that":[170],"TSP":[171],"significantly":[172],"improves":[173],"performance":[175],"existing":[177],"popular":[178],"architectures":[179],"(PointNet,":[180],"PointNet++,":[181],"DGCNN)":[182],"large":[184],"scene":[185],"benchmarks":[187],"(S3DIS,":[188],"ScanNet)":[189],"part":[191],"dataset":[193],"ShapeNet.":[194]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}