{"id":"https://openalex.org/W7164037266","doi":"https://doi.org/10.48550/arxiv.2606.08744","title":"MB-Loc: Multi-planar Bird's-eye-view Localization in outdoor LiDAR scenes","display_name":"MB-Loc: Multi-planar Bird's-eye-view Localization in outdoor LiDAR scenes","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7164037266","doi":"https://doi.org/10.48550/arxiv.2606.08744"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08744","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08744","pdf_url":null,"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":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.2606.08744","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138240408","display_name":"Ayaan Choudhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choudhury, Ayaan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135149595","display_name":"Preet Savalia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Savalia, Preet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116424483","display_name":"Anirudh Pydah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pydah, Anirudh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138232350","display_name":"Avinash Sharma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharma, Avinash","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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9664999842643738,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9664999842643738,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.009700000286102295,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0044999998062849045,"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/lidar","display_name":"Lidar","score":0.6430000066757202},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5848000049591064},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41769999265670776},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.414900004863739},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3921999931335449},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.3774000108242035},{"id":"https://openalex.org/keywords/rotation","display_name":"Rotation (mathematics)","score":0.3472999930381775},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3046000003814697}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7351999878883362},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6430000066757202},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5848000049591064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5105999708175659},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4884999990463257},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.3774000108242035},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3046000003814697},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08744","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08744","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08744","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08744","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Global":[0],"LiDAR":[1,81],"localization":[2],"is":[3],"a":[4,64,84,131],"fundamental":[5],"task":[6],"for":[7],"autonomous":[8],"navigation":[9],"systems.":[10],"Recent":[11],"methods":[12],"perform":[13,167],"Scene":[14],"Coordinate":[15],"Regression":[16,25],"(SCR)":[17],"and":[18,49,66,98,176],"achieve":[19],"superior":[20],"accuracy":[21],"over":[22],"Absolute":[23],"Pose":[24],"(APR)":[26],"solutions":[27],"by":[28],"predicting":[29],"dense":[30],"3D":[31,47,75,109,151],"world":[32],"coordinates.":[33],"However,":[34],"SCR":[35,68],"approaches":[36],"introduce":[37,130],"two":[38],"major":[39],"bottlenecks:":[40],"severe":[41],"computational":[42,115,198],"inefficiency":[43],"from":[44],"processing":[45],"raw":[46],"geometries":[48],"significant":[50],"performance":[51],"degradation":[52],"under":[53],"varying":[54],"sensor":[55],"viewpoints.":[56],"To":[57,121],"address":[58],"these":[59],"limitations,":[60],"we":[61,77,129,149],"present":[62],"MB-Loc,":[63],"lightweight":[65],"viewpoint-robust":[67],"framework.":[69],"Instead":[70],"of":[71,117,126],"relying":[72],"on":[73,170],"heavy":[74],"convolutions,":[76],"project":[78],"the":[79,93,96,114,123,159,171,183],"input":[80],"scan":[82],"into":[83,102],"2.5D":[85],"Multi-planar":[86],"Bird's-Eye":[87],"View":[88],"(BEV)":[89],"representation.":[90],"By":[91],"slicing":[92],"point-cloud":[94],"along":[95],"Z-axis":[97],"mapping":[99],"signed":[100],"depths":[101],"discrete":[103],"2D":[104,119],"planes,":[105],"MB-Loc":[106,191],"retains":[107],"essential":[108],"geometric":[110],"structures":[111],"while":[112],"exploiting":[113],"tractability":[116],"standard":[118],"CNNs.":[120],"handle":[122],"inherent":[124],"sparsity":[125],"outdoor":[127],"LiDAR,":[128],"KL-regularized":[132],"latent":[133],"bottleneck":[134],"that":[135,178],"explicitly":[136],"models":[137],"spatial":[138,152],"uncertainty":[139],"without":[140],"injecting":[141],"stochastic":[142],"noise.":[143],"Finally,":[144],"to":[145,155,161],"ensure":[146],"rotation":[147],"robustness,":[148],"apply":[150],"augmentations":[153],"prior":[154],"planar":[156],"projection,":[157],"forcing":[158],"network":[160],"implicitly":[162],"learn":[163],"viewpoint-invariant":[164],"features.":[165],"We":[166],"extensive":[168],"experiments":[169],"publicly":[172],"available":[173],"NCLT":[174],"dataset":[175],"demonstrate":[177],"our":[179],"proposed":[180],"method":[181],"outperforms":[182,193],"current":[184],"state-of-the-art.":[185],"Operating":[186],"at":[187],"real-time":[188],"inference":[189],"speeds,":[190],"significantly":[192],"traditional":[194],"3D-SCR":[195],"architectures":[196],"in":[197],"efficiency.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}