{"id":"https://openalex.org/W2997890711","doi":"https://doi.org/10.1109/tgrs.2019.2958517","title":"TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation","display_name":"TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation","publication_year":2019,"publication_date":"2019-12-24","ids":{"openalex":"https://openalex.org/W2997890711","doi":"https://doi.org/10.1109/tgrs.2019.2958517","mag":"2997890711"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2019.2958517","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2958517","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","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/A5100414383","display_name":"Ying Li","orcid":"https://orcid.org/0000-0003-0608-9619"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Ying Li","raw_affiliation_strings":["Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032270954","display_name":"Lingfei Ma","orcid":"https://orcid.org/0000-0001-8893-9693"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Lingfei Ma","raw_affiliation_strings":["Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073939201","display_name":"Zilong Zhong","orcid":"https://orcid.org/0000-0003-0104-9116"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zilong Zhong","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031991761","display_name":"Dongpu Cao","orcid":"https://orcid.org/0000-0001-7929-4336"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Dongpu Cao","raw_affiliation_strings":["Waterloo Cognitive Autonomous Driving Lab, University of Waterloo, Waterloo, ON, Canada","Waterloo Cognitive Autonomous Driving Lab, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"Waterloo Cognitive Autonomous Driving Lab, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Waterloo Cognitive Autonomous Driving Lab, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100613889","display_name":"Jonathan Li","orcid":"https://orcid.org/0000-0001-7899-0049"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jonathan Li","raw_affiliation_strings":["Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","Department of System Design Engineering, University of Waterloo, Waterloo, Canada","Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Department of System Design Engineering, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100414383"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":10.2559,"has_fulltext":false,"cited_by_count":133,"citation_normalized_percentile":{"value":0.99255751,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"58","issue":"5","first_page":"3588","last_page":"3600"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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":0.9998000264167786,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8286994695663452},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5748239159584045},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5659049153327942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5311896800994873},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5255502462387085},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4719109833240509},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.44138258695602417},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4384183883666992},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.4309196472167969},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3339044153690338},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1902480125427246}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8286994695663452},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5748239159584045},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5659049153327942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5311896800994873},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5255502462387085},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4719109833240509},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.44138258695602417},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4384183883666992},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.4309196472167969},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3339044153690338},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1902480125427246},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2019.2958517","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2958517","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6399999856948853}],"awards":[{"id":"https://openalex.org/G1205905451","display_name":null,"funder_award_id":"RGPIN-50503-10284","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"},{"id":"https://openalex.org/G1552547945","display_name":null,"funder_award_id":"41871380","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1662382123","https://openalex.org/W1903029394","https://openalex.org/W1920022804","https://openalex.org/W1988115241","https://openalex.org/W2022394120","https://openalex.org/W2124592697","https://openalex.org/W2147880316","https://openalex.org/W2161236525","https://openalex.org/W2271840356","https://openalex.org/W2460657278","https://openalex.org/W2519887557","https://openalex.org/W2556802233","https://openalex.org/W2558460151","https://openalex.org/W2558748708","https://openalex.org/W2560609797","https://openalex.org/W2594519801","https://openalex.org/W2606202972","https://openalex.org/W2768308213","https://openalex.org/W2795014656","https://openalex.org/W2797650883","https://openalex.org/W2798965597","https://openalex.org/W2800466466","https://openalex.org/W2803937221","https://openalex.org/W2805687669","https://openalex.org/W2885482233","https://openalex.org/W2887121482","https://openalex.org/W2888754481","https://openalex.org/W2895472109","https://openalex.org/W2902302021","https://openalex.org/W2952793010","https://openalex.org/W2960986959","https://openalex.org/W2962928871","https://openalex.org/W2963021451","https://openalex.org/W2963121255","https://openalex.org/W2963158438","https://openalex.org/W2963226018","https://openalex.org/W2963281829","https://openalex.org/W2963333168","https://openalex.org/W2963517242","https://openalex.org/W2963640720","https://openalex.org/W2963727135","https://openalex.org/W2963830382","https://openalex.org/W2964015378","https://openalex.org/W2964062501","https://openalex.org/W2964121744","https://openalex.org/W2964311892","https://openalex.org/W2964339842","https://openalex.org/W2979750740","https://openalex.org/W2981199548","https://openalex.org/W3124633807","https://openalex.org/W4285719527","https://openalex.org/W4293509836","https://openalex.org/W6631190155","https://openalex.org/W6637178625","https://openalex.org/W6640300118","https://openalex.org/W6682082992","https://openalex.org/W6694517276","https://openalex.org/W6726873649","https://openalex.org/W6739778489","https://openalex.org/W6747904511","https://openalex.org/W6750943524","https://openalex.org/W6752783436","https://openalex.org/W6763422710","https://openalex.org/W6789519889"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2093471820","https://openalex.org/W50079190","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2111726165","https://openalex.org/W2347460059","https://openalex.org/W1984858032","https://openalex.org/W2616891703","https://openalex.org/W2349179172"],"abstract_inverted_index":{"Recent":[0],"geometric":[1,27,75,86,121,128],"deep":[2,163],"learning":[3],"works":[4],"define":[5],"convolution":[6,55,92],"operations":[7],"in":[8,43],"local":[9,74,91,115,125],"regions":[10],"and":[11,21,32,72,97,118,195,211,225],"have":[12],"enjoyed":[13],"remarkable":[14],"success":[15],"on":[16,93,156,208,218],"non-Euclidean":[17],"data,":[18],"including":[19],"graph":[20,54],"point":[22,78,95,116,190],"clouds.":[23,79],"However,":[24],"the":[25,30,111,114,119,179,185,201],"high-level":[26],"correlations":[28],"between":[29],"input":[31],"its":[33,167],"neighboring":[34,120],"coordinates":[35],"or":[36],"features":[37,76,117,122,129,144,164],"are":[38,98,108,130],"not":[39],"fully":[40],"exploited,":[41],"resulting":[42],"suboptimal":[44],"segmentation":[45,186],"performance.":[46],"In":[47],"this":[48],"article,":[49],"we":[50,58],"propose":[51],"a":[52,101,138,171],"novel":[53],"architecture,":[56],"which":[57],"term":[59],"as":[60,110],"Taylor":[61,135],"Gaussian":[62,133],"mixture":[63],"model":[64],"(GMM)":[65],"network":[66],"(TGNet),":[67],"to":[68,145,159,182],"efficiently":[69],"learn":[70],"expressive":[71],"compositional":[73],"from":[77,124],"The":[80],"TGNet":[81,153],"is":[82,176],"composed":[83],"of":[84,103,113],"basic":[85],"units,":[87],"TGConv,":[88],"that":[89,200],"conduct":[90],"irregular":[94],"sets":[96],"parametrized":[99],"by":[100,132],"family":[102],"filters.":[104],"Specifically,":[105],"these":[106],"filters":[107],"defined":[109],"products":[112],"extracted":[123],"coordinates.":[126],"These":[127],"expressed":[131],"weighted":[134],"kernels.":[136],"Then,":[137],"parametric":[139],"pooling":[140],"layer":[141,181],"aggregates":[142],"TGConv":[143,155],"generate":[146],"new":[147],"feature":[148],"vectors":[149],"for":[150],"each":[151],"point.":[152],"employs":[154],"multiscale":[157],"neighborhoods":[158],"extract":[160],"coarse-to-fine":[161],"semantic":[162],"while":[165],"improving":[166],"scale":[168],"invariance.":[169],"Additionally,":[170],"conditional":[172],"random":[173],"field":[174],"(CRF)":[175],"adopted":[177],"within":[178],"output":[180],"further":[183],"improve":[184],"results.":[187],"Using":[188],"three":[189],"cloud":[191],"data":[192,227],"sets,":[193,228],"qualitative":[194],"quantitative":[196],"experimental":[197],"results":[198],"demonstrate":[199],"proposed":[202],"method":[203],"achieves":[204],"62.2%":[205],"average":[206],"accuracy":[207],"ScanNet,":[209],"57.8%":[210],"68.17%":[212],"mean":[213],"intersection":[214],"over":[215],"union":[216],"(mIoU)":[217],"Stanford":[219],"Large-Scale":[220],"3D":[221],"Indoor":[222],"Spaces":[223],"(S3DIS)":[224],"Paris-Lille-3D":[226],"respectively.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":6}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}