{"id":"https://openalex.org/W4387559375","doi":"https://doi.org/10.48550/arxiv.2310.04787","title":"HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields","display_name":"HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields","publication_year":2023,"publication_date":"2023-10-07","ids":{"openalex":"https://openalex.org/W4387559375","doi":"https://doi.org/10.48550/arxiv.2310.04787"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2310.04787","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.04787","pdf_url":"https://arxiv.org/pdf/2310.04787","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2310.04787","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100695077","display_name":"Wei Zhang","orcid":"https://orcid.org/0009-0006-3519-1419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090933045","display_name":"Tiecheng Sun","orcid":"https://orcid.org/0000-0002-6813-4756"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Tiecheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115594573","display_name":"Sen Wang","orcid":"https://orcid.org/0000-0002-0593-2902"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Sen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100630779","display_name":"Qing Cheng","orcid":"https://orcid.org/0009-0007-9117-6800"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049647675","display_name":"Norbert Haala","orcid":"https://orcid.org/0000-0002-7745-337X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haala, Norbert","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":true,"cited_by_count":1,"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9986000061035156,"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":0.9976999759674072,"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/simultaneous-localization-and-mapping","display_name":"Simultaneous localization and mapping","score":0.7328256368637085},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7157090902328491},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6730510592460632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6595726013183594},{"id":"https://openalex.org/keywords/bundle-adjustment","display_name":"Bundle adjustment","score":0.5163832902908325},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4891531765460968},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4838472902774811},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.475065678358078},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.45501768589019775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41812509298324585},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10930830240249634},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.1068570613861084},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0764840841293335}],"concepts":[{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.7328256368637085},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7157090902328491},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6730510592460632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6595726013183594},{"id":"https://openalex.org/C179458375","wikidata":"https://www.wikidata.org/wiki/Q1020763","display_name":"Bundle adjustment","level":3,"score":0.5163832902908325},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4891531765460968},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4838472902774811},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.475065678358078},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.45501768589019775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41812509298324585},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10930830240249634},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.1068570613861084},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0764840841293335},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2310.04787","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.04787","pdf_url":"https://arxiv.org/pdf/2310.04787","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2310.04787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.04787","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":"pmh:oai:arXiv.org:2310.04787","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.04787","pdf_url":"https://arxiv.org/pdf/2310.04787","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387559375.pdf","grobid_xml":"https://content.openalex.org/works/W4387559375.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2992292799","https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W2416513692","https://openalex.org/W3122088529","https://openalex.org/W2980723633","https://openalex.org/W3033220023","https://openalex.org/W2963200201"],"abstract_inverted_index":{"In":[0],"this":[1],"letter,":[2],"we":[3,84,116,144],"present":[4],"a":[5,64,152],"neural":[6,22,49,65,82],"field-based":[7,66],"real-time":[8,191],"monocular":[9,147],"mapping":[10,23],"framework":[11],"for":[12],"accurate":[13],"and":[14,18,60,89,104,132,136,156,174,186],"dense":[15,54],"Simultaneous":[16],"Localization":[17],"Mapping":[19],"(SLAM).":[20],"Recent":[21],"frameworks":[24],"show":[25],"promising":[26],"results,":[27],"but":[28],"rely":[29],"on":[30,72],"RGB-D":[31],"or":[32,35],"pose":[33,121,135],"inputs,":[34],"cannot":[36],"run":[37,128],"in":[38,167,184],"real-time.":[39],"To":[40,139],"address":[41],"these":[42],"limitations,":[43],"our":[44,53,179],"approach":[45,56,126,180],"integrates":[46],"dense-SLAM":[47],"with":[48],"implicit":[50],"fields.":[51],"Specifically,":[52],"SLAM":[55,75],"runs":[57],"parallel":[58],"tracking":[59],"global":[61,108,114],"optimization,":[62],"while":[63,189],"map":[67,101,187],"is":[68],"constructed":[69],"incrementally":[70],"based":[71],"the":[73,78,100,134,163],"latest":[74],"estimates.":[76],"For":[77,113],"efficient":[79,119],"construction":[80],"of":[81],"fields,":[83],"employ":[85],"multi-resolution":[86],"grid":[87],"encoding":[88],"signed":[90],"distance":[91],"function":[92],"(SDF)":[93],"representation.":[94],"This":[95],"allows":[96],"us":[97],"to":[98,107,127,161],"keep":[99],"always":[102],"up-to-date":[103],"adapt":[105],"instantly":[106],"updates":[109],"via":[110],"loop":[111,130],"closing.":[112],"consistency,":[115],"propose":[117,151],"an":[118],"Sim(3)-based":[120],"graph":[122],"bundle":[123],"adjustment":[124,158],"(PGBA)":[125],"online":[129],"closing":[131],"mitigate":[133],"scale":[137,157,164],"drift.":[138],"enhance":[140],"depth":[141,148,155,168],"accuracy":[142,185],"further,":[143],"incorporate":[145],"learned":[146],"priors.":[149,169],"We":[150],"novel":[153],"joint":[154],"(JDSA)":[159],"module":[160],"solve":[162],"ambiguity":[165],"inherent":[166],"Extensive":[170],"evaluations":[171],"across":[172],"synthetic":[173],"real-world":[175],"datasets":[176],"validate":[177],"that":[178],"outperforms":[181],"existing":[182],"methods":[183],"completeness":[188],"preserving":[190],"performance.":[192]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}