{"id":"https://openalex.org/W4200632008","doi":"https://doi.org/10.1609/aaai.v36i3.20194","title":"Behind the Curtain: Learning Occluded Shapes for 3D Object Detection","display_name":"Behind the Curtain: Learning Occluded Shapes for 3D Object Detection","publication_year":2022,"publication_date":"2022-06-28","ids":{"openalex":"https://openalex.org/W4200632008","doi":"https://doi.org/10.1609/aaai.v36i3.20194"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i3.20194","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/20194/19953","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/20194/19953","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030369632","display_name":"Qiangeng Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiangeng Xu","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085287748","display_name":"Yiqi Zhong","orcid":"https://orcid.org/0000-0002-0928-8018"},"institutions":[{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqi Zhong","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082235583","display_name":"Ulrich Neumann","orcid":"https://orcid.org/0000-0001-8977-7112"},"institutions":[{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ulrich Neumann","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.059,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":102,"citation_normalized_percentile":{"value":0.999831,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"36","issue":"3","first_page":"2893","last_page":"2901"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998,"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":0.9998,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9849,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9812,"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/occupancy-grid-mapping","display_name":"Occupancy grid mapping","score":0.69626355},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6866032},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.48833963},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.45945814},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.41879418}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.85932314},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7830763},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7481281},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7331995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72064495},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.69626355},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6866032},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.63492644},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.55433524},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.48833963},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.48063082},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.45945814},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.41879418},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38908696},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.20701644},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.18107733},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14701727},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12973061},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.12106782},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.090516806},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.076544344},{"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":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i3.20194","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/20194/19953","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2112.02205","pdf_url":"https://arxiv.org/pdf/2112.02205","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i3.20194","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/20194/19953","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.56}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":42,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2115579991","https://openalex.org/W2555618208","https://openalex.org/W2624273542","https://openalex.org/W2883363148","https://openalex.org/W2884561390","https://openalex.org/W2897529137","https://openalex.org/W2917353338","https://openalex.org/W2948080642","https://openalex.org/W2949708697","https://openalex.org/W2963243172","https://openalex.org/W2963727135","https://openalex.org/W2964325922","https://openalex.org/W2968296999","https://openalex.org/W2981949127","https://openalex.org/W2988715931","https://openalex.org/W2997188997","https://openalex.org/W2997814983","https://openalex.org/W3006754479","https://openalex.org/W3008105217","https://openalex.org/W3034236957","https://openalex.org/W3034239557","https://openalex.org/W3034494113","https://openalex.org/W3034602892","https://openalex.org/W3034681945","https://openalex.org/W3035002114","https://openalex.org/W3035172746","https://openalex.org/W3035709245","https://openalex.org/W3036853234","https://openalex.org/W3089401811","https://openalex.org/W3107212734","https://openalex.org/W3107819843","https://openalex.org/W3108426750","https://openalex.org/W3109675406","https://openalex.org/W3118341329","https://openalex.org/W3124432240","https://openalex.org/W3130463448","https://openalex.org/W3139602483","https://openalex.org/W3174692508","https://openalex.org/W3174981916","https://openalex.org/W3202229469","https://openalex.org/W4287331103"],"related_works":["https://openalex.org/W4390524233","https://openalex.org/W4287027631","https://openalex.org/W4237171675","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W3192357901","https://openalex.org/W3036286480","https://openalex.org/W2952736415","https://openalex.org/W2883297582","https://openalex.org/W2387360586"],"abstract_inverted_index":{"Advances":[0],"in":[1,24,78],"LiDAR":[2,20],"sensors":[3],"provide":[4],"rich":[5],"3D":[6,10,40,51,123,160],"data":[7],"that":[8,73,86,104],"supports":[9],"scene":[11],"understanding.":[12],"However,":[13],"due":[14],"to":[15,39,136],"occlusion":[16,90],"and":[17,67,91,112,120,147,165],"signal":[18,92],"miss,":[19],"point":[21,79],"clouds":[22],"are":[23,74,87],"practice":[25],"2.5D":[26],"as":[27],"they":[28],"cover":[29],"only":[30],"partial":[31],"underlying":[32],"shapes,":[33],"which":[34,61],"poses":[35],"a":[36,48,107],"fundamental":[37],"challenge":[38],"perception.":[41],"To":[42],"tackle":[43],"the":[44,57,63,69,84,100,126,132,144,148,153,159,168,175],"challenge,":[45],"we":[46],"present":[47],"novel":[49],"LiDAR-based":[50],"object":[52,64,71,110],"detection":[53,118,161],"model,":[54],"dubbed":[55],"Behind":[56],"Curtain":[58],"Detector":[59],"(BtcDet),":[60],"learns":[62],"shape":[65],"priors":[66],"estimates":[68],"complete":[70],"shapes":[72,111],"partially":[75],"occluded":[76],"(curtained)":[77],"clouds.":[80],"BtcDet":[81,171],"first":[82],"identifies":[83],"regions":[85],"affected":[88],"by":[89,179],"miss.":[93],"In":[94],"these":[95],"regions,":[96],"our":[97],"model":[98],"predicts":[99],"probability":[101,115],"of":[102,155,162,174],"occupancy":[103,127],"indicates":[105],"if":[106],"region":[108],"contains":[109],"integrates":[113],"this":[114],"map":[116],"with":[117],"features":[119],"generates":[121],"high-quality":[122],"proposals.":[124],"Finally,":[125],"estimation":[128],"is":[129,183],"integrated":[130],"into":[131],"proposal":[133],"refinement":[134],"module":[135],"generate":[137],"accurate":[138],"bounding":[139],"boxes.":[140],"Extensive":[141],"experiments":[142],"on":[143,167],"KITTI":[145,169],"Dataset":[146,151],"Waymo":[149],"Open":[150],"demonstrate":[152],"effectiveness":[154],"BtcDet.":[156],"Particularly":[157],"for":[158],"both":[163],"cars":[164],"cyclists":[166],"benchmark,":[170],"surpasses":[172],"all":[173],"published":[176],"state-of-the-art":[177],"methods":[178],"remarkable":[180],"margins.":[181],"Code":[182],"released.":[184]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4200632008","counts_by_year":[{"year":2024,"cited_by_count":44},{"year":2023,"cited_by_count":44},{"year":2022,"cited_by_count":14}],"updated_date":"2025-01-06T02:20:23.354940","created_date":"2021-12-31"}