{"id":"https://openalex.org/W7133720714","doi":"https://doi.org/10.48550/arxiv.2603.03935","title":"DISC: Dense Integrated Semantic Context for Large-Scale Open-Set Semantic Mapping","display_name":"DISC: Dense Integrated Semantic Context for Large-Scale Open-Set Semantic Mapping","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133720714","doi":"https://doi.org/10.48550/arxiv.2603.03935"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.03935","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054740214","display_name":"Felix Igelbrink","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Igelbrink, Felix","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092397676","display_name":"Lennart Niecksch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niecksch, Lennart","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128179705","display_name":"Martin Atzmueller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atzmueller, Martin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020594579","display_name":"Joachim Hertzberg","orcid":"https://orcid.org/0000-0002-6371-9624"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hertzberg, Joachim","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5054740214"],"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":0.29989999532699585,"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":0.29989999532699585,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2685999870300293,"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":0.2468000054359436,"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/semantic-mapping","display_name":"Semantic mapping","score":0.6689000129699707},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6204000115394592},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5422000288963318},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.44999998807907104},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.42320001125335693},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4212999939918518},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4034999907016754},{"id":"https://openalex.org/keywords/semantic-data-model","display_name":"Semantic data model","score":0.3984000086784363},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3172000050544739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8179000020027161},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.6689000129699707},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6204000115394592},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5174000263214111},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.44999998807907104},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.42320001125335693},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4212999939918518},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28679999709129333},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C103692084","wikidata":"https://www.wikidata.org/wiki/Q1765824","display_name":"Semantic grid","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.27889999747276306},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.27230000495910645},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2711000144481659},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C137314826","wikidata":"https://www.wikidata.org/wiki/Q2330408","display_name":"Data mapping","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.03935","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.03935","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03935","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.03935","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Open-set":[0],"semantic":[1,67,139],"mapping":[2],"enables":[3],"language-driven":[4],"robotic":[5,151],"perception,":[6],"but":[7],"current":[8,133],"instance-centric":[9],"approaches":[10],"are":[11],"bottlenecked":[12],"by":[13],"context-depriving":[14],"and":[15,57,106,141,159],"computationally":[16],"expensive":[17],"crop-based":[18],"feature":[19],"extraction.":[20],"To":[21,69],"overcome":[22],"this":[23],"fundamental":[24],"limitation,":[25],"we":[26],"introduce":[27],"DISC":[28,78,130],"(Dense":[29],"Integrated":[30],"Semantic":[31],"Context),":[32],"featuring":[33],"a":[34,82,107,145],"novel":[35],"single-pass,":[36],"distance-weighted":[37],"extraction":[38],"mechanism.":[39],"By":[40],"deriving":[41],"high-fidelity":[42],"CLIP":[43],"embeddings":[44],"directly":[45],"from":[46],"the":[47,55],"vision":[48],"transformer's":[49],"intermediate":[50],"layers,":[51],"our":[52,99],"approach":[53,100],"eliminates":[54],"latency":[56],"domain-shift":[58],"artifacts":[59],"of":[60],"traditional":[61],"image":[62],"cropping,":[63],"yielding":[64],"pure,":[65],"mask-aligned":[66],"representations.":[68],"fully":[70,83],"leverage":[71],"these":[72],"features":[73],"in":[74,123,137],"large-scale":[75],"continuous":[76],"mapping,":[77],"is":[79],"built":[80],"upon":[81],"GPU-accelerated":[84],"architecture":[85],"that":[86,129],"replaces":[87],"periodic":[88],"offline":[89],"processing":[90],"with":[91],"precise,":[92],"on-the-fly":[93],"voxel-level":[94],"instance":[95],"refinement.":[96],"We":[97],"evaluate":[98],"on":[101,113],"standard":[102],"benchmarks":[103],"(Replica,":[104],"ScanNet)":[105],"newly":[108],"generated":[109],"large-scale-mapping":[110],"dataset":[111],"based":[112],"Habitat-Matterport":[114],"3D":[115],"(HM3DSEM)":[116],"to":[117],"assess":[118],"scalability":[119],"across":[120],"complex":[121],"scenes":[122],"multi-story":[124],"buildings.":[125],"Extensive":[126],"evaluations":[127],"demonstrate":[128],"significantly":[131],"surpasses":[132],"state-of-the-art":[134],"zero-shot":[135],"methods":[136],"both":[138],"accuracy":[140],"query":[142],"retrieval,":[143],"providing":[144],"robust,":[146],"real-time":[147],"capable":[148],"framework":[149],"for":[150],"deployment.":[152],"The":[153],"full":[154],"source":[155],"code,":[156],"data":[157],"generation":[158],"evaluation":[160],"pipelines":[161],"will":[162],"be":[163],"made":[164],"available":[165],"at":[166],"https://github.com/DFKI-NI/DISC.":[167]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-06T00:00:00"}