{"id":"https://openalex.org/W7165901191","doi":"https://doi.org/10.48550/arxiv.2606.25652","title":"Auto-Labelling-Based Domain Transfer for 3D Object Detection on a Bicycle-Mounted LiDAR Platform","display_name":"Auto-Labelling-Based Domain Transfer for 3D Object Detection on a Bicycle-Mounted LiDAR Platform","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7165901191","doi":"https://doi.org/10.48550/arxiv.2606.25652"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.25652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25652","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.25652","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123182871","display_name":"Mario Finkbeiner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Finkbeiner, Mario","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030706111","display_name":"Max A. Buettner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Buettner, Max A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123190417","display_name":"Kanak Mazumder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mazumder, Kanak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139344397","display_name":"Fabian B. Flohr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Flohr, Fabian B.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9309999942779541,"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":0.9309999942779541,"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":0.02879999950528145,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.007400000002235174,"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/lidar","display_name":"Lidar","score":0.7516000270843506},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7329999804496765},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7192000150680542},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6075999736785889},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5936999917030334},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5649999976158142},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.49129998683929443},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4650000035762787}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7516000270843506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7400000095367432},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7329999804496765},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7192000150680542},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6075999736785889},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5947999954223633},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5936999917030334},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5684000253677368},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.3140000104904175},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.258899986743927},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.25652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25652","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.25652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25652","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8078603148460388,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"3D":[1,63,86,97,196],"perception":[2],"of":[3,90,153,181],"vulnerable":[4],"road":[5],"users":[6],"(VRUs)":[7],"such":[8],"as":[9],"cyclists":[10],"and":[11,21,123,171,173,198],"pedestrians":[12,170],"is":[13,69,144],"essential":[14],"for":[15,25,194,206],"their":[16,120],"safety":[17],"in":[18,31,104,119],"urban":[19,105],"traffic":[20,42],"a":[22,44,66,85,131,191,203,214],"core":[23],"requirement":[24],"autonomous":[26],"driving":[27],"(AD).":[28],"Alongside":[29],"advances":[30],"vehicle-based":[32],"perception,":[33],"research":[34],"increasingly":[35],"equips":[36],"bicycles":[37],"with":[38,165],"sensors":[39],"to":[40,47,76,162,213],"study":[41],"from":[43,65,100],"perspective":[45,68],"native":[46],"VRUs.":[48],"Such":[49],"platforms":[50],"still":[51],"rely":[52],"on":[53,58,126,146,169],"LiDAR":[54,93],"detectors":[55,74,111,176,212],"originally":[56],"trained":[57,186],"vehicle":[59],"data,":[60],"yet":[61],"annotated":[62,92],"data":[64],"cyclist's":[67],"scarce.":[70],"How":[71],"well":[72],"these":[73],"generalise":[75],"this":[77],"setting":[78],"has":[79],"not":[80],"been":[81],"evaluated.":[82],"We":[83,107],"present":[84],"object":[87],"detection":[88,197],"benchmark":[89,189],"1,027":[91],"keyframes":[94],"(over":[95],"18,000":[96],"bounding":[98],"boxes)":[99],"the":[101,147,166,174,179,182],"FUSE-Bike":[102],"platform":[103],"Munich.":[106],"evaluate":[108],"four":[109],"nuScenes-pre-trained":[110],"against":[112],"1,854":[113],"human-verified":[114],"ground-truth":[115],"(GT)":[116],"boxes":[117],"both":[118],"original":[121],"form":[122],"after":[124],"finetuning":[125],"training":[127],"labels":[128],"produced":[129],"by":[130,160],"VRU-dedicated":[132],"auto-labelling":[133],"pipeline":[134],"that":[135,200],"requires":[136],"no":[137],"manual":[138,207],"annotation.":[139],"The":[140,188],"zero-shot":[141],"domain":[142],"gap":[143],"concentrated":[145],"VRU":[148],"classes.":[149],"Finetuning":[150],"recovers":[151],"most":[152],"it,":[154],"improving":[155],"mean":[156],"average":[157],"precision":[158],"(mAP)":[159],"up":[161],"23.4":[163],"points":[164],"largest":[167],"gains":[168],"cyclists,":[172],"adapted":[175],"even":[177],"surpass":[178],"quality":[180],"auto-labels":[183,201],"they":[184],"were":[185],"on.":[187],"provides":[190],"reproducible":[192],"baseline":[193],"VRU-centric":[195],"shows":[199],"are":[202],"viable":[204],"substitute":[205],"annotation":[208],"when":[209],"adapting":[210],"vehicle-trained":[211],"cyclist":[215],"platform.":[216]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-26T00:00:00"}
