{"id":"https://openalex.org/W7170585866","doi":"https://doi.org/10.48550/arxiv.2607.21309","title":"Factorized Spatio-Temporal Convolutions for Human Pose Estimation from Planar Lidar","display_name":"Factorized Spatio-Temporal Convolutions for Human Pose Estimation from Planar Lidar","publication_year":2026,"publication_date":"2026-07-23","ids":{"openalex":"https://openalex.org/W7170585866","doi":"https://doi.org/10.48550/arxiv.2607.21309"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.21309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.21309","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2607.21309","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094111527","display_name":"Simone Arreghini","orcid":"https://orcid.org/0009-0004-1700-4355"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arreghini, Simone","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058862171","display_name":"Mirko Nava","orcid":"https://orcid.org/0000-0002-3736-0419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nava, Mirko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093930758","display_name":"Nicholas Carlotti","orcid":"https://orcid.org/0009-0006-1475-3400"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlotti, Nicholas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143568441","display_name":"Antonio Paolillo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paolillo, Antonio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052119291","display_name":"Alessandro Giusti","orcid":"https://orcid.org/0000-0003-1240-0768"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Giusti, Alessandro","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":0.26190000772476196,"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":0.26190000772476196,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.24549999833106995,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.20569999516010284,"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":0.8238000273704529},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5968999862670898},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.5321000218391418},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5267000198364258},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.520799994468689},{"id":"https://openalex.org/keywords/planar","display_name":"Planar","score":0.42669999599456787},{"id":"https://openalex.org/keywords/omnidirectional-antenna","display_name":"Omnidirectional antenna","score":0.3944999873638153},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.3871000111103058},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.36800000071525574}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8238000273704529},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7074000239372253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.704800009727478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038999795913696},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5968999862670898},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.5321000218391418},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C134786449","wikidata":"https://www.wikidata.org/wiki/Q3391255","display_name":"Planar","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C24027999","wikidata":"https://www.wikidata.org/wiki/Q2176348","display_name":"Omnidirectional antenna","level":3,"score":0.3944999873638153},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3578000068664551},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C2776228582","wikidata":"https://www.wikidata.org/wiki/Q7455797","display_name":"Service robot","level":3,"score":0.32670000195503235},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.263700008392334},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.21309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.21309","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.21309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.21309","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Localizing":[0],"nearby":[1],"humans":[2],"and":[3,14,24,42,51,93,139,158],"estimating":[4],"their":[5],"facing":[6],"direction":[7],"are":[8,34],"key":[9],"capabilities":[10],"for":[11,116,166],"safe":[12],"navigation":[13],"socially":[15],"aware":[16],"human-robot":[17],"interaction.":[18],"Many":[19],"pose-estimation":[20],"pipelines":[21],"target":[22],"cameras":[23],"3D":[25],"LiDAR":[26,58,85,118],"or":[27],"assume":[28],"GPU-class":[29],"compute,":[30],"whereas":[31],"service":[32,156,172],"robots":[33],"often":[35],"equipped":[36],"only":[37],"with":[38,60,160],"omnidirectional":[39,48],"planar":[40,57],"LiDARs":[41],"modest":[43],"onboard":[44],"processors.":[45],"We":[46,96,142],"address":[47],"human":[49,90],"detection":[50],"relative":[52,94],"2D":[53],"pose":[54],"estimation":[55],"from":[56,76,102],"sequences":[59,86],"a":[61,103,128,155],"lightweight":[62],"network":[63,82],"based":[64],"on":[65,145,154,169],"Space-Time":[66],"Blocks,":[67],"which":[68],"explicitly":[69],"separate":[70],"spatial":[71,167],"processing":[72],"along":[73],"scan":[74],"rays":[75],"temporal":[77],"aggregation":[78],"across":[79],"scans.":[80],"Our":[81],"processes":[83],"360\u00b0":[84],"to":[87],"output":[88],"per-ray":[89],"presence,":[91],"distance,":[92],"orientation.":[95],"train":[97],"it":[98],"via":[99],"cross-modal":[100],"self-supervision":[101],"narrow":[104],"RGB-D":[105],"body":[106],"tracker":[107],"in":[108,134],"the":[109,114,146],"sensors'":[110],"overlap":[111],"region,":[112],"removing":[113],"need":[115],"manual":[117],"labels.":[119],"Quantitative":[120],"experiments":[121],"show":[122],"that":[123],"our":[124],"approach":[125],"consistently":[126],"outperforms":[127],"parameter-matched":[129],"baseline":[130],"model,":[131],"reducing":[132],"errors":[133],"distance":[135],"(-38%),":[136],"position":[137],"(-28%),":[138],"orientation":[140],"(-15%).":[141],"further":[143],"benchmark":[144],"public":[147],"FROG":[148],"dataset,":[149],"report":[150],"real-time":[151],"CPU":[152],"inference":[153],"robot,":[157],"validate":[159],"in-field":[161],"demonstrations,":[162],"supporting":[163],"its":[164],"suitability":[165],"perception":[168],"computationally":[170],"constrained":[171],"robots.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-25T00:00:00"}