{"id":"https://openalex.org/W4389665962","doi":"https://doi.org/10.1109/iros55552.2023.10341958","title":"LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection","display_name":"LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4389665962","doi":"https://doi.org/10.1109/iros55552.2023.10341958"},"language":"en","primary_location":{"id":"doi:10.1109/iros55552.2023.10341958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros55552.2023.10341958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5100604494","display_name":"Tong He","orcid":"https://orcid.org/0000-0003-2772-9320"},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Tong He","raw_affiliation_strings":["Waymo LLC"],"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060736311","display_name":"Pei Sun","orcid":"https://orcid.org/0000-0002-4651-6227"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei Sun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003789189","display_name":"Zhaoqi Leng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaoqi Leng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387431","display_name":"Chenxi Liu","orcid":"https://orcid.org/0000-0003-3613-1662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenxi Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081024054","display_name":"Dragomir Anguelov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dragomir Anguelov","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110774377","display_name":"Mingxing Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mingxing Tan","raw_affiliation_strings":["Waymo LLC"],"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100604494"],"corresponding_institution_ids":["https://openalex.org/I4210145145"],"apc_list":null,"apc_paid":null,"fwci":0.7399,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.73917989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1637","last_page":"1644"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9984999895095825,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9969000220298767,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7577842473983765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7475533485412598},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7053786516189575},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.653444230556488},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.594238817691803},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5897325277328491},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5790326595306396},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5604537725448608},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5253046154975891},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4617486298084259},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44687703251838684},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.43694978952407837},{"id":"https://openalex.org/keywords/foreground-detection","display_name":"Foreground detection","score":0.426597535610199},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08764654397964478},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.08463230729103088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577842473983765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7475533485412598},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7053786516189575},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.653444230556488},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.594238817691803},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5897325277328491},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5790326595306396},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5604537725448608},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5253046154975891},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4617486298084259},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44687703251838684},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.43694978952407837},{"id":"https://openalex.org/C2779769447","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Foreground detection","level":4,"score":0.426597535610199},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08764654397964478},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.08463230729103088},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros55552.2023.10341958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros55552.2023.10341958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":48,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2150066425","https://openalex.org/W2560609797","https://openalex.org/W2624273542","https://openalex.org/W2897529137","https://openalex.org/W2908510526","https://openalex.org/W2949708697","https://openalex.org/W2963120444","https://openalex.org/W2963121255","https://openalex.org/W2963727135","https://openalex.org/W2964062501","https://openalex.org/W2968296999","https://openalex.org/W2970259716","https://openalex.org/W3034314779","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3035750285","https://openalex.org/W3107212734","https://openalex.org/W3114753236","https://openalex.org/W3117901461","https://openalex.org/W3136022415","https://openalex.org/W3138516171","https://openalex.org/W3164543136","https://openalex.org/W3167095230","https://openalex.org/W3167539120","https://openalex.org/W3167732492","https://openalex.org/W3171377125","https://openalex.org/W3175563878","https://openalex.org/W3205005447","https://openalex.org/W4214755140","https://openalex.org/W4214777292","https://openalex.org/W4310078553","https://openalex.org/W4312307873","https://openalex.org/W4312501532","https://openalex.org/W4312894406","https://openalex.org/W4312916565","https://openalex.org/W4313024968","https://openalex.org/W4313064206","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6739663239","https://openalex.org/W6739778489","https://openalex.org/W6739901393","https://openalex.org/W6757817989","https://openalex.org/W6760424586","https://openalex.org/W6763422710","https://openalex.org/W6766799972","https://openalex.org/W6800161232"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W4293094720","https://openalex.org/W2739701376","https://openalex.org/W2123129869","https://openalex.org/W2360918960"],"abstract_inverted_index":{"We":[0,111,134],"propose":[1,113],"a":[2,30,65,114],"late-to-early":[3,61],"recurrent":[4,66],"feature":[5,35,62],"fusion":[6,36,63],"scheme":[7],"for":[8,48,129,157],"3D":[9,31,149],"object":[10,32,150],"detection":[11,151],"using":[12],"temporal":[13],"LiDAR":[14],"point":[15],"clouds.":[16],"Our":[17,58],"main":[18],"motivation":[19],"is":[20,69],"fusing":[21],"object-aware":[22],"latent":[23],"embeddings":[24],"into":[25,105],"the":[26,39,44,93,121,139,153,158],"early":[27],"stages":[28],"of":[29,95,161],"detector.":[33],"This":[34,68],"strategy":[37],"enables":[38],"model":[40,101,122],"to":[41,103,123],"better":[42],"capture":[43],"shapes":[45],"and":[46,79,145],"poses":[47],"challenging":[49,159],"objects,":[50],"compared":[51],"with":[52],"learning":[53],"from":[54],"raw":[55],"points":[56],"directly.":[57],"method":[59,137],"conducts":[60],"in":[64],"manner.":[67],"achieved":[70],"by":[71,109],"enforcing":[72],"window-based":[73],"attention":[74],"blocks":[75],"upon":[76],"temporally":[77],"calibrated":[78],"aligned":[80],"sparse":[81,96],"pillar":[82,89],"tokens.":[83],"Leveraging":[84],"bird's":[85],"eye":[86],"view":[87],"foreground":[88],"segmentation,":[90],"we":[91],"reduce":[92],"number":[94],"history":[97],"features":[98],"that":[99],"our":[100,136],"needs":[102],"fuse":[104],"its":[106],"current":[107],"frame":[108,125],"10x.":[110],"also":[112],"stochastic-length":[115],"FrameDrop":[116],"training":[117],"technique,":[118],"which":[119],"generalizes":[120],"variable":[124],"lengths":[126],"at":[127],"inference":[128],"improved":[130],"performance":[131],"without":[132],"retraining.":[133],"evaluate":[135],"on":[138,148],"widely":[140],"adopted":[141],"Waymo":[142],"Open":[143],"Dataset":[144],"demonstrate":[146],"improvement":[147],"against":[152],"baseline":[154],"model,":[155],"especially":[156],"category":[160],"large":[162],"objects.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}