{"id":"https://openalex.org/W4308080366","doi":"https://doi.org/10.1109/itsc55140.2022.9922046","title":"Supervoxel-based and Cost-Effective Active Learning for Point Cloud Semantic Segmentation","display_name":"Supervoxel-based and Cost-Effective Active Learning for Point Cloud Semantic Segmentation","publication_year":2022,"publication_date":"2022-10-08","ids":{"openalex":"https://openalex.org/W4308080366","doi":"https://doi.org/10.1109/itsc55140.2022.9922046"},"language":"en","primary_location":{"id":"doi:10.1109/itsc55140.2022.9922046","doi":"https://doi.org/10.1109/itsc55140.2022.9922046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc55140.2022.9922046","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"IEEE International Conference on Intelligent Transportation Systems (ITSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5001485069","display_name":"Shanding Ye","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanding Ye","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102027966","display_name":"Yongjian Fu","orcid":"https://orcid.org/0000-0002-8976-6066","observed_orcids":["https://orcid.org/0000-0002-8976-6066"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjian Fu","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073179274","display_name":"Hu Lin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Lin","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101799443","display_name":"Zhe Yin","orcid":"https://orcid.org/0000-0003-2379-6323","observed_orcids":["https://orcid.org/0000-0003-2379-6323"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Yin","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101876496","display_name":"Zhijie Pan","orcid":"https://orcid.org/0000-0002-3466-7423","observed_orcids":["https://orcid.org/0000-0002-3466-7423"]},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijie Pan","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.3143,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.4979102,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1030","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.6054999828338623,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.6054999828338623,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.19869999587535858,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.09319999814033508,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud-semantic-segmentation","display_name":"point cloud semantic segmentation","score":0.9419999718666077},{"id":"https://openalex.org/keywords/active-learning-machine-learning","display_name":"active learning (machine learning)","score":0.9980000257492065},{"id":"https://openalex.org/keywords/supervoxels","display_name":"supervoxels","score":0.9300000071525574},{"id":"https://openalex.org/keywords/annotation-cost-reduction","display_name":"annotation cost reduction","score":0.7229999899864197},{"id":"https://openalex.org/keywords/point-cloud-intensity","display_name":"point cloud intensity","score":0.6370000243186951},{"id":"https://openalex.org/keywords/unsupervised-segmentation","display_name":"unsupervised segmentation","score":0.6850000023841858},{"id":"https://openalex.org/keywords/semantickitti","display_name":"SemanticKITTI","score":0.5979999899864197},{"id":"https://openalex.org/keywords/3d-point-clouds","display_name":"3D point clouds","score":0.6449999809265137}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8248659372329712},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8121132850646973},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7671785354614258},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7192307114601135},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6929042339324951},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6203945875167847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6040241718292236},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5992797017097473},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5786111950874329},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5434662103652954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4508596658706665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4282756447792053},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41470950841903687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc55140.2022.9922046","doi":"https://doi.org/10.1109/itsc55140.2022.9922046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc55140.2022.9922046","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"IEEE International Conference on Intelligent Transportation Systems (ITSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1513874326","https://openalex.org/W1553262910","https://openalex.org/W1904365287","https://openalex.org/W1970531977","https://openalex.org/W1978633512","https://openalex.org/W2034747098","https://openalex.org/W2080021732","https://openalex.org/W2101365302","https://openalex.org/W2119720396","https://openalex.org/W2124244761","https://openalex.org/W2125951969","https://openalex.org/W2128678390","https://openalex.org/W2132948751","https://openalex.org/W2135249503","https://openalex.org/W2160821342","https://openalex.org/W2167314816","https://openalex.org/W2262342046","https://openalex.org/W2287655724","https://openalex.org/W2471138382","https://openalex.org/W2556802233","https://openalex.org/W2585292948","https://openalex.org/W2594519801","https://openalex.org/W2606864979","https://openalex.org/W2624503621","https://openalex.org/W2740823339","https://openalex.org/W2774918944","https://openalex.org/W2789833952","https://openalex.org/W2797997528","https://openalex.org/W2798820905","https://openalex.org/W2899747753","https://openalex.org/W2942476750","https://openalex.org/W2944173897","https://openalex.org/W2951768055","https://openalex.org/W2951786554","https://openalex.org/W2965803762","https://openalex.org/W2983697263","https://openalex.org/W2989750479","https://openalex.org/W2995306638","https://openalex.org/W3012494314","https://openalex.org/W3047263508","https://openalex.org/W3080353943","https://openalex.org/W3085923845","https://openalex.org/W3123332468","https://openalex.org/W3159676894","https://openalex.org/W3184557372","https://openalex.org/W3198372861","https://openalex.org/W3203978358","https://openalex.org/W3205626500"],"related_works":["https://openalex.org/W4366168590","https://openalex.org/W3123332468","https://openalex.org/W7084103605","https://openalex.org/W4399464615","https://openalex.org/W4304015009","https://openalex.org/W4417143366","https://openalex.org/W4408238587","https://openalex.org/W4390871922","https://openalex.org/W3047263508","https://openalex.org/W3203978358"],"abstract_inverted_index":{"Recent":[0],"successes":[1],"in":[2],"point":[3,26,37,50,93,108],"cloud":[4,27,38,51,94,109],"semantic":[5],"segmentation":[6],"heavily":[7],"rely":[8],"on":[9,157],"a":[10,18,36,61,92,141],"large":[11],"amount":[12],"of":[13,49,140],"annotated":[14],"data":[15,28,153],"to":[16,70,96,106,137,155],"train":[17],"deep":[19,145],"neural":[20,146],"network.":[21],"Furthermore,":[22],"three":[23],"dimensional":[24],"(3D)":[25],"generally":[29],"has":[30],"no":[31],"order":[32],"and":[33,35,63,74,133],"sparsity,":[34],"often":[39],"includes":[40],"more":[41],"than":[42],"ten":[43],"thousand":[44],"points,":[45],"thus":[46],"increasing":[47],"difficulties":[48],"annotation.":[52,79],"To":[53,80],"reduce":[54],"the":[55,88,113],"huge":[56],"annotation":[57],"efforts,":[58],"we":[59,85,134],"propose":[60,105],"supervoxel-based":[62],"cost-effective":[64],"active":[65,130],"learning":[66,131],"pipeline":[67],"which":[68],"aims":[69],"select":[71],"only":[72,149],"uncertain":[73],"diverse":[75],"segmented":[76,97,114],"regions":[77,98],"for":[78,117],"better":[81],"exploit":[82],"annotating":[83,89],"budget,":[84],"first":[86],"change":[87],"units":[90],"from":[91],"scan":[95],"through":[99],"two":[100],"unsupervised":[101],"methods.":[102],"We":[103],"further":[104],"leverage":[107],"intensity":[110],"when":[111],"calculating":[112],"region":[115,119],"information":[116],"encouraging":[118],"diversity.":[120],"Extensive":[121],"experiments":[122],"show":[123],"that":[124],"our":[125],"approach":[126],"greatly":[127],"outperforms":[128],"previous":[129],"methods,":[132],"achieve":[135],"up":[136],"90%":[138],"performance":[139],"fully":[142],"supervised":[143],"trained":[144],"network":[147],"by":[148],"using":[150],"3%":[151],"labeled":[152],"compared":[154],"100%":[156],"SemanticKITTI":[158],"dataset.":[159]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2022-11-08T00:00:00"}