{"id":"https://openalex.org/W7123348351","doi":"https://doi.org/10.1109/access.2026.3651795","title":"SGSPose: Neuromorphic-Geometric 6D Pose Estimation Through Spiking Graph Neural Networks and SE(3)-Equivariant Learning","display_name":"SGSPose: Neuromorphic-Geometric 6D Pose Estimation Through Spiking Graph Neural Networks and SE(3)-Equivariant Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123348351","doi":"https://doi.org/10.1109/access.2026.3651795"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3651795","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651795","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3651795","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118977818","display_name":"Janhavi Chaurasia","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Janhavi Chaurasia","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121168812","display_name":"Eshaan Rithesh Adyanthaya","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Eshaan Rithesh Adyanthaya","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066513950","display_name":"Manas Ranjan Prusty","orcid":"https://orcid.org/0000-0003-2704-505X"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manas Ranjan Prusty","raw_affiliation_strings":["Centre for Cyber Physical Systems, Vellore Institute of Technology (VIT), Chennai, India"],"affiliations":[{"raw_affiliation_string":"Centre for Cyber Physical Systems, Vellore Institute of Technology (VIT), Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5118977818"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08290241,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"7130","last_page":"7136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.296999990940094,"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":0.296999990940094,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.0738999992609024,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.07100000232458115,"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/pose","display_name":"Pose","score":0.8461999893188477},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7451000213623047},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.5580000281333923},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46860000491142273},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4674000144004822},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.4634000062942505},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46059998869895935},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4499000012874603},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43479999899864197}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.8461999893188477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8036999702453613},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7451000213623047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7401999831199646},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.5580000281333923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46860000491142273},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.4634000062942505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4499000012874603},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3774999976158142},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C33010914","wikidata":"https://www.wikidata.org/wiki/Q7291980","display_name":"Random neural network","level":4,"score":0.29100000858306885},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2849000096321106},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3651795","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651795","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3651795","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651795","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.797761082649231}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2200124539","https://openalex.org/W2474281075","https://openalex.org/W2584731199","https://openalex.org/W2795645133","https://openalex.org/W3178144853","https://openalex.org/W4367297892","https://openalex.org/W4383108429","https://openalex.org/W4389665589","https://openalex.org/W4391630742","https://openalex.org/W4394692374","https://openalex.org/W4402728454","https://openalex.org/W4402951712","https://openalex.org/W4407191199","https://openalex.org/W4413308766"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"estimation":[1,73,159],"of":[2,12,59,64,80,120,145,156],"6D":[3,157],"poses,":[4],"including":[5],"both":[6,55],"the":[7,56,81,87,95,121,143,153],"spatial":[8],"position":[9],"and":[10,24,48,61,70,109,129],"orientation":[11],"objects":[13],"or":[14],"cameras,":[15],"is":[16,117],"a":[17,31],"significant":[18],"challenge":[19],"in":[20,74,136],"robotics,":[21],"augmented":[22],"reality,":[23],"autonomous":[25],"systems.":[26],"This":[27],"study":[28],"introduces":[29],"SGSPose,":[30],"novel":[32],"architecture":[33],"that":[34,86],"integrates":[35],"spiking":[36],"neural":[37,43],"network":[38,44],"(SNN)":[39],"feature":[40],"encoding,":[41],"graph":[42],"(GNN)":[45],"relational":[46],"reasoning,":[47],"SE(3)-equivariant":[49,65],"Lie":[50],"algebra":[51],"optimization.":[52],"By":[53],"leveraging":[54],"event-driven":[57],"efficiency":[58],"SNNs":[60],"geometric":[62],"consistency":[63],"GNNs,":[66],"SGSPose":[67,88],"achieves":[68,90],"robust":[69],"energy-efficient":[71],"pose":[72,158],"complex":[75,137],"real-world":[76],"environments.":[77,139],"Extensive":[78],"evaluation":[79],"challenging":[82],"7Scenes":[83,96],"dataset":[84],"demonstrates":[85],"method":[89],"state-of-the-art":[91],"translation":[92,100],"accuracy":[93],"on":[94],"benchmark,":[97],"reducing":[98],"mean":[99,111],"errors":[101,113],"by":[102],"over":[103],"70%":[104],"compared":[105],"to":[106],"existing":[107],"techniques":[108],"attaining":[110],"rotation":[112],"within":[114,118],"8\u201321\u00b0,":[115],"which":[116],"10-30%":[119],"leading":[122],"methods":[123],"across":[124],"scenes,":[125],"demonstrating":[126],"its":[127],"robustness":[128],"effectiveness":[130],"for":[131,151],"precise":[132],"6-DoF":[133],"camera":[134],"relocalization":[135],"indoor":[138],"These":[140],"results":[141],"highlight":[142],"potential":[144],"brain-inspired":[146],"geometry-aware":[147],"deep":[148],"learning":[149],"frameworks":[150],"advancing":[152],"next":[154],"generation":[155],"technologies.":[160]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-01-14T00:00:00"}
