{"id":"https://openalex.org/W3020472769","doi":"https://doi.org/10.1109/iv47402.2020.9304631","title":"Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study","display_name":"Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3020472769","doi":"https://doi.org/10.1109/iv47402.2020.9304631","mag":"3020472769"},"language":"en","primary_location":{"id":"doi:10.1109/iv47402.2020.9304631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.11803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Larissa T. Triess","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Larissa T. Triess","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":null,"display_name":"David Peter","orcid":null},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"David Peter","raw_affiliation_strings":["Mercedes-Benz AG, Research and Development, Stuttgart, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG, Research and Development, Stuttgart, Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Christoph B. Rist","orcid":null},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph B. Rist","raw_affiliation_strings":["Mercedes-Benz AG, Research and Development, Stuttgart, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG, Research and Development, Stuttgart, Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"last","author":{"id":null,"display_name":"J. Marius Zollner","orcid":null},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J. Marius Zollner","raw_affiliation_strings":["Research Center for Information Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Information Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I143379178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":2.1575,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.89508637,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1116","last_page":"1121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9983000159263611,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.743399977684021},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6956999897956848},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6383000016212463},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6140000224113464},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5618000030517578},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5081999897956848},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.436599999666214},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4341000020503998}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7724999785423279},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.743399977684021},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6956999897956848},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6383000016212463},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6251999735832214},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6140000224113464},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5618000030517578},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40880000591278076},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.272599995136261}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iv47402.2020.9304631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.11803","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.11803","pdf_url":"https://arxiv.org/pdf/2004.11803","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.11803","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.11803","pdf_url":"https://arxiv.org/pdf/2004.11803","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1987869189","https://openalex.org/W2005286252","https://openalex.org/W2032498155","https://openalex.org/W2145287260","https://openalex.org/W2150066425","https://openalex.org/W2161236525","https://openalex.org/W2395611524","https://openalex.org/W2624503621","https://openalex.org/W2795374598","https://openalex.org/W2798297823","https://openalex.org/W2888754481","https://openalex.org/W2910281775","https://openalex.org/W2960986959","https://openalex.org/W2962912109","https://openalex.org/W2963182550","https://openalex.org/W2963281829","https://openalex.org/W2963881378","https://openalex.org/W2964216646","https://openalex.org/W2968557240","https://openalex.org/W2981199548","https://openalex.org/W2990613095","https://openalex.org/W2991216808","https://openalex.org/W3003437478","https://openalex.org/W6696085341","https://openalex.org/W6736894448","https://openalex.org/W6738035320","https://openalex.org/W6763422710"],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"vehicles":[1],"need":[2],"to":[3,16,30,46,51,78,83,114],"have":[4],"a":[5,38,61,110,134,147,154,169,176,199,204],"semantic":[6,32,67],"understanding":[7],"of":[8,22,65,96,172,179,183,198,207,218,231],"the":[9,23,80,94,142,163,184,189,195,208,213,225,232],"three-dimensional":[10],"world":[11],"around":[12],"them":[13],"in":[14,37,192,220],"order":[15],"reason":[17],"about":[18],"their":[19],"environment.":[20],"State":[21],"art":[24],"methods":[25,210],"use":[26,52,133],"deep":[27],"neural":[28],"networks":[29],"predict":[31],"classes":[33],"for":[34,70,162],"each":[35],"point":[36,72,121],"LiDAR":[39,48,71,200],"scan.":[40,201],"A":[41],"powerful":[42],"and":[43,82,99],"efficient":[44],"way":[45],"process":[47],"measurements":[49],"is":[50],"two-dimensional,":[53],"image-like":[54],"projections.":[55],"In":[56,146],"this":[57],"work,":[58],"we":[59,92,117,150,167],"perform":[60,151],"comprehensive":[62],"experimental":[63],"study":[64],"image-based":[66],"segmentation":[68,222],"architectures":[69],"clouds.":[73],"We":[74,132,202],"demonstrate":[75],"various":[76],"techniques":[77],"boost":[79],"performance":[81,223],"improve":[84],"runtime":[85],"as":[86,88],"well":[87],"memory":[89],"constraints.":[90],"First,":[91],"examine":[93],"effect":[95],"network":[97],"size":[98],"suggest":[100],"that":[101,125,138,159],"much":[102],"faster":[103],"inference":[104,234],"times":[105],"can":[106],"be":[107],"achieved":[108],"at":[109,141],"very":[111],"low":[112],"cost":[113],"accuracy.":[115],"Next,":[116],"introduce":[118],"an":[119,216],"improved":[120],"cloud":[122],"projection":[123],"technique":[124],"does":[126],"not":[127],"suffer":[128],"from":[129],"systematic":[130],"occlusions.":[131],"cyclic":[135],"padding":[136],"mechanism":[137],"provides":[139],"context":[140],"horizontal":[143],"field-of-view":[144],"boundaries.":[145],"third":[148],"part,":[149],"experiments":[152],"with":[153,175,211],"soft":[155],"Dice":[156],"loss":[157],"function":[158],"directly":[160],"optimizes":[161],"intersection-over-union":[164],"metric.":[165],"Finally,":[166],"propose":[168,203],"new":[170],"kind":[171],"convolution":[173],"layer":[174],"reduced":[177],"amount":[178],"weight-sharing":[180],"along":[181,194],"one":[182],"two":[185],"spatial":[186],"dimensions,":[187],"addressing":[188],"large":[190],"difference":[191],"appearance":[193],"vertical":[196],"axis":[197],"final":[205],"set":[206],"above":[209],"which":[212],"model":[214],"achieves":[215],"increase":[217],"3.2%":[219],"mIoU":[221],"over":[224],"baseline":[226],"while":[227],"requiring":[228],"only":[229],"42%":[230],"original":[233],"time.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2020-05-01T00:00:00"}
