{"id":"https://openalex.org/W7163709696","doi":"https://doi.org/10.48550/arxiv.2606.05833","title":"Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models","display_name":"Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163709696","doi":"https://doi.org/10.48550/arxiv.2606.05833"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05833","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05833","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.2606.05833","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137962636","display_name":"Haibo Wang","orcid":"https://orcid.org/0000-0002-3624-2648"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haibo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137949343","display_name":"Lifu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Lifu","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.7857999801635742,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.7857999801635742,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.03669999912381172,"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"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.02669999934732914,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6219000220298767},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5907999873161316},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.45669999718666077},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.448199987411499},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4011000096797943},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.39899998903274536},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.3953999876976013},{"id":"https://openalex.org/keywords/spatial-intelligence","display_name":"Spatial intelligence","score":0.39489999413490295}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404999732971191},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6304000020027161},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6219000220298767},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5907999873161316},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.45669999718666077},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.448199987411499},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.39489999413490295},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C104065381","wikidata":"https://www.wikidata.org/wiki/Q1002535","display_name":"Geometric modeling","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34610000252723694},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C7305733","wikidata":"https://www.wikidata.org/wiki/Q207961","display_name":"Geometric shape","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.30090001225471497},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C3018868096","wikidata":"https://www.wikidata.org/wiki/Q2693233","display_name":"Internal consistency","level":3,"score":0.2743000090122223},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05833","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05833","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.2606.05833","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05833","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":{"Multimodal":[0],"Large":[1],"Language":[2],"Models":[3],"(MLLMs)":[4],"excel":[5],"at":[6],"2D":[7,48],"semantic":[8,56],"understanding":[9],"but":[10],"lack":[11],"intrinsic":[12],"3D":[13,34,85,134,157],"awareness,":[14],"resulting":[15],"in":[16],"representations":[17,45,75,153],"that":[18,42,166],"fail":[19],"to":[20,61,107,117,136],"maintain":[21],"geometric":[22,44,100,148],"and":[23,130,147],"spatial":[24,63,162,180],"consistency":[25],"across":[26],"video":[27,49],"frames.":[28],"Given":[29],"the":[30,55,73,77,138,150],"scarcity":[31],"of":[32,76],"large-scale":[33],"data,":[35],"we":[36],"present":[37],"GeoVR,":[38],"a":[39,92,123,172],"novel":[40],"framework":[41],"learns":[43],"using":[46],"purely":[47],"sequences.":[50],"This":[51,88],"approach":[52],"effectively":[53],"restructures":[54],"latent":[57],"space":[58],"within":[59],"MLLMs":[60],"unlock":[62],"intelligence.":[64,181],"Rather":[65],"than":[66],"employing":[67],"superficial":[68],"feature":[69,140],"mixing,":[70],"GeoVR":[71,167],"reshapes":[72],"internal":[74,152],"MLLM":[78],"by":[79,97,143],"distilling":[80,132],"geometry":[81],"knowledge":[82],"from":[83],"pre-trained":[84],"foundation":[86,177],"models.":[87],"is":[89],"accomplished":[90],"through":[91],"multi-objective":[93],"learning":[94],"strategy":[95],"driven":[96],"four":[98],"complementary":[99],"targets:":[101],"(1)":[102],"estimating":[103],"inter-frame":[104],"camera":[105],"poses":[106],"embed":[108],"varying":[109],"viewpoint":[110],"dynamics,":[111],"(2)":[112],"regressing":[113],"dense":[114],"depth":[115],"maps":[116],"anchor":[118],"physical":[119,146],"distances,":[120],"(3)":[121],"predicting":[122],"metric":[124],"scale":[125],"factor":[126],"for":[127,175],"real-world":[128],"calibration,":[129],"(4)":[131],"multi-scale":[133],"features":[135],"align":[137],"intermediate":[139],"space.":[141],"Guided":[142],"these":[144],"explicit":[145],"constraints,":[149],"model's":[151],"naturally":[154],"develop":[155],"strong":[156],"awareness.":[158],"Extensive":[159],"experiments":[160],"on":[161],"reasoning":[163],"benchmarks":[164],"demonstrate":[165],"achieves":[168],"state-of-the-art":[169],"performance,":[170],"establishing":[171],"new":[173],"paradigm":[174],"endowing":[176],"models":[178],"with":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}