{"id":"https://openalex.org/W3120891544","doi":"https://doi.org/10.1109/iv47402.2020.9304655","title":"High Dimensional Frustum PointNet for 3D Object Detection from Camera, LiDAR, and Radar","display_name":"High Dimensional Frustum PointNet for 3D Object Detection from Camera, LiDAR, and Radar","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3120891544","doi":"https://doi.org/10.1109/iv47402.2020.9304655","mag":"3120891544"},"language":"en","primary_location":{"id":"doi:10.1109/iv47402.2020.9304655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304655","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":["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/A5020537192","display_name":"Leichen Wang","orcid":"https://orcid.org/0000-0001-8011-6123"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Leichen Wang","raw_affiliation_strings":["University Konstanz, Konstanz, Germany"],"affiliations":[{"raw_affiliation_string":"University Konstanz, Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026892089","display_name":"Tianbai Chen","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":false,"raw_author_name":"Tianbai Chen","raw_affiliation_strings":["Faculty of Electrical Engineering, KIT, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, KIT, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024387495","display_name":"Carsten Anklam","orcid":null},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Carsten Anklam","raw_affiliation_strings":["Research and Development of Radar Sensor, Daimler AG, Germany"],"affiliations":[{"raw_affiliation_string":"Research and Development of Radar Sensor, Daimler AG, Germany","institution_ids":["https://openalex.org/I891521709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080296321","display_name":"Bastian Goldl\u00fcecke","orcid":"https://orcid.org/0000-0003-3427-4029"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bastian Goldluecke","raw_affiliation_strings":["University Konstanz, Konstanz, Germany"],"affiliations":[{"raw_affiliation_string":"University Konstanz, Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5020537192"],"corresponding_institution_ids":["https://openalex.org/I189712700"],"apc_list":null,"apc_paid":null,"fwci":2.1591,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.89883114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1621","last_page":"1628"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9951000213623047,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9922000169754028,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7623634338378906},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7570995092391968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7104604244232178},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6726442575454712},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6174893975257874},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6127166152000427},{"id":"https://openalex.org/keywords/frustum","display_name":"Frustum","score":0.5136041641235352},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5027978420257568},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.4316054880619049},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.426662415266037},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41754746437072754},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.4169076979160309},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41159164905548096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2648504972457886},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.22836148738861084},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11609911918640137},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1075914204120636}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7623634338378906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7570995092391968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7104604244232178},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6726442575454712},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6174893975257874},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6127166152000427},{"id":"https://openalex.org/C97402662","wikidata":"https://www.wikidata.org/wiki/Q846235","display_name":"Frustum","level":2,"score":0.5136041641235352},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5027978420257568},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.4316054880619049},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.426662415266037},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41754746437072754},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.4169076979160309},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41159164905548096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2648504972457886},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.22836148738861084},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11609911918640137},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1075914204120636},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv47402.2020.9304655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304655","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1920022804","https://openalex.org/W2095381174","https://openalex.org/W2108598243","https://openalex.org/W2163071539","https://openalex.org/W2415454270","https://openalex.org/W2555618208","https://openalex.org/W2560609797","https://openalex.org/W2741069557","https://openalex.org/W2742562184","https://openalex.org/W2791763440","https://openalex.org/W2885482233","https://openalex.org/W2891529090","https://openalex.org/W2891649842","https://openalex.org/W2894705404","https://openalex.org/W2897529137","https://openalex.org/W2904140919","https://openalex.org/W2904592809","https://openalex.org/W2908973513","https://openalex.org/W2919534447","https://openalex.org/W2928999112","https://openalex.org/W2944605902","https://openalex.org/W2949708697","https://openalex.org/W2954258401","https://openalex.org/W2962731536","https://openalex.org/W2962888833","https://openalex.org/W2963083779","https://openalex.org/W2963121255","https://openalex.org/W2963292632","https://openalex.org/W2963400571","https://openalex.org/W2963727135","https://openalex.org/W2964062501","https://openalex.org/W2967098543","https://openalex.org/W2968296999","https://openalex.org/W2969987486","https://openalex.org/W2970095196","https://openalex.org/W2974922121","https://openalex.org/W2980547801","https://openalex.org/W2981857055","https://openalex.org/W2995042771","https://openalex.org/W2995549977","https://openalex.org/W2996167479","https://openalex.org/W3002820529","https://openalex.org/W3004237909","https://openalex.org/W3034239557","https://openalex.org/W3035574168","https://openalex.org/W3117804044","https://openalex.org/W3169902865","https://openalex.org/W6640300118","https://openalex.org/W6739778489","https://openalex.org/W6754918364","https://openalex.org/W6763422710","https://openalex.org/W6769343902","https://openalex.org/W6771471907","https://openalex.org/W6771922894","https://openalex.org/W6797210932"],"related_works":["https://openalex.org/W2090425280","https://openalex.org/W4289815017","https://openalex.org/W2065795160","https://openalex.org/W2047933837","https://openalex.org/W2902617670","https://openalex.org/W2347325618","https://openalex.org/W4297193075","https://openalex.org/W3147274046","https://openalex.org/W2470216081","https://openalex.org/W4293218651"],"abstract_inverted_index":{"Fusing":[0],"the":[1,41,48,56,66,70,124,134],"raw":[2,67],"data":[3,21,50,68],"from":[4,69,84,95],"different":[5,18],"automotive":[6],"sensors":[7,111],"for":[8,36],"real-world":[9],"environment":[10],"perception":[11],"is":[12],"still":[13],"challenging":[14],"due":[15],"to":[16,112],"their":[17],"representations":[19],"and":[20,52,63,73,82,102],"formats.":[22],"In":[23,75],"this":[24],"work,":[25],"we":[26],"propose":[27],"a":[28,87,96],"novel":[29],"method":[30],"termed":[31],"High":[32],"Dimensional":[33],"Frustum":[34],"PointNet":[35],"3D":[37,126],"object":[38,114],"detection":[39,115,127],"in":[40],"context":[42],"of":[43,55,133],"autonomous":[44],"driving.":[45],"Motivated":[46],"by":[47,119],"goals":[49],"diversity":[51],"lossless":[53],"processing":[54],"data,":[57],"our":[58,129],"deep":[59],"learning":[60],"approach":[61],"directly":[62],"jointly":[64],"uses":[65],"camera,":[71],"LiDAR,":[72],"radar.":[74],"more":[76],"detail,":[77],"given":[78],"2D":[79],"region":[80],"proposals":[81],"classification":[83],"camera":[85],"images,":[86],"high":[88],"dimensional":[89],"convolution":[90],"operator":[91],"captures":[92],"local":[93],"features":[94],"point":[97],"cloud":[98],"enhanced":[99],"with":[100],"color":[101],"temporal":[103],"information.":[104],"Radars":[105],"are":[106],"used":[107],"as":[108],"adaptive":[109],"plug-in":[110],"refine":[113],"performance.":[116],"As":[117],"shown":[118],"an":[120],"extensive":[121],"evaluation":[122],"on":[123],"nuScenes":[125],"benchmark,":[128],"network":[130],"outperforms":[131],"most":[132],"previous":[135],"methods.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":4}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2025-10-10T00:00:00"}
