{"id":"https://openalex.org/W7150980013","doi":"https://doi.org/10.48550/arxiv.2604.02903","title":"RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection","display_name":"RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7150980013","doi":"https://doi.org/10.48550/arxiv.2604.02903"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02903","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.2604.02903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133003254","display_name":"Cheng Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101348403","display_name":"Mingqian Ji","orcid":"https://orcid.org/0009-0006-1855-1945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Mingqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133007833","display_name":"Shanshan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shanshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133038810","display_name":"Zhihao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133049699","display_name":"Jian Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jian","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":0.8623999953269958,"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":0.8623999953269958,"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":0.04500000178813934,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.008899999782443047,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/serialization","display_name":"Serialization","score":0.8952000141143799},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6306999921798706},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.61080002784729},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5156999826431274},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.3930000066757202},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3910999894142151},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.367900013923645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3587999939918518}],"concepts":[{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.8952000141143799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8052999973297119},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6306999921798706},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.61080002784729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5916000008583069},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3930000066757202},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3910999894142151},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32989999651908875},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30630001425743103},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C2988656282","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity detection","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02903","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.2604.02903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02903","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":[{"id":"https://metadata.un.org/sdg/11","score":0.7303277254104614,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Long-range":[0],"3D":[1,74],"object":[2],"detection":[3],"remains":[4],"challenging":[5,141],"because":[6],"LiDAR":[7],"observations":[8],"become":[9],"highly":[10],"sparse":[11,59,78],"and":[12,93,106,118,135,147],"fragmented":[13],"in":[14,58,139],"the":[15,140],"far":[16],"field,":[17],"making":[18],"reliable":[19],"context":[20,95],"modeling":[21,39],"difficult":[22],"for":[23,72,96],"existing":[24],"detectors.":[25,75],"To":[26,61],"address":[27,62],"this":[28,63],"issue,":[29,64],"recent":[30],"state":[31],"space":[32],"model":[33],"(SSM)-based":[34],"methods":[35],"have":[36],"improved":[37],"long-range":[38],"efficiency.":[40],"However,":[41],"their":[42],"effectiveness":[43],"is":[44,101],"still":[45],"limited":[46],"by":[47],"generic":[48],"serialization":[49,87],"strategies":[50],"that":[51],"fail":[52],"to":[53,132,156],"preserve":[54],"meaningful":[55],"contextual":[56],"neighborhoods":[57],"scenes.":[60],"we":[65],"propose":[66],"RayMamba,":[67],"a":[68,85],"geometry-aware":[69],"plug-and-play":[70],"enhancement":[71],"voxel-based":[73],"RayMamba":[76,129],"organizes":[77],"voxels":[79],"into":[80],"sector-wise":[81],"ordered":[82],"sequences":[83],"through":[84],"ray-aligned":[86],"strategy,":[88],"which":[89],"preserves":[90],"directional":[91],"continuity":[92],"occlusion-related":[94],"subsequent":[97],"Mamba-based":[98],"modeling.":[99],"It":[100],"compatible":[102],"with":[103],"both":[104],"LiDAR-only":[105],"multimodal":[107],"detectors,":[108],"while":[109],"introducing":[110],"only":[111],"modest":[112],"overhead.":[113],"Extensive":[114],"experiments":[115],"on":[116,145,151],"nuScenes":[117],"Argoverse":[119,152],"2":[120,153],"demonstrate":[121],"consistent":[122],"improvements":[123],"across":[124],"strong":[125],"baselines.":[126],"In":[127],"particular,":[128],"achieves":[130],"up":[131],"2.49":[133],"mAP":[134],"1.59":[136],"NDS":[137],"gain":[138],"40--50":[142],"m":[143],"range":[144],"nuScenes,":[146],"further":[148],"improves":[149],"VoxelNeXt":[150],"from":[154],"30.3":[155],"31.2":[157],"mAP.":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-07T00:00:00"}