{"id":"https://openalex.org/W7160824469","doi":"https://doi.org/10.48550/arxiv.2605.07356","title":"UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition","display_name":"UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160824469","doi":"https://doi.org/10.48550/arxiv.2605.07356"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07356","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07356","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.2605.07356","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135853972","display_name":"Shuai Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114201382","display_name":"Zhecheng Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Zhecheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135890959","display_name":"Zhuxiao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhuxiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102501411","display_name":"Ou Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ou, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135842176","display_name":"Tengxi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tengxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135843386","display_name":"Yuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135878577","display_name":"Wufan Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Wufan","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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.4927000105381012,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.4927000105381012,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.16599999368190765,"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":0.13289999961853027,"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/segmentation","display_name":"Segmentation","score":0.6625999808311462},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5860000252723694},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.48170000314712524},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4374000132083893},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42489999532699585},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4018000066280365},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.4000000059604645},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.39320001006126404},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.3779999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7674000263214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6884999871253967},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6625999808311462},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5860000252723694},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4374000132083893},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42399999499320984},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.39320001006126404},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3562000095844269},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.3100999891757965},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.296999990940094},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2669000029563904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07356","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07356","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.2605.07356","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07356","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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8127771019935608}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,67,181],"of":[2,24,53],"large-scale":[3],"3D":[4,55,66],"point":[5],"clouds":[6],"is":[7,209],"crucial":[8],"for":[9,84],"applications":[10],"such":[11],"as":[12],"autonomous":[13],"driving":[14],"and":[15,26,37,65,104,118,132,158,167,174],"urban":[16],"digital":[17],"twins.":[18],"However,":[19],"the":[20,27,43,54,60,122,133,150,172],"sparse":[21],"sampling":[22],"pattern":[23],"LiDAR":[25],"view-dependent":[28],"geometric":[29,98,105],"distortion":[30],"in":[31,180],"image":[32],"observations":[33],"complicate":[34],"cross-modal":[35,156],"alignment":[36,166],"hinder":[38],"stable":[39,198],"fusion.":[40],"Inspired":[41],"by":[42,49,188],"fact":[44],"that":[45,59,138],"2D":[46,64],"images":[47],"captured":[48],"cameras":[50],"are":[51,113,139],"representations":[52],"world,":[56],"we":[57],"recognize":[58],"features":[61,109,152],"learned":[62],"from":[63,110],"share":[68],"some":[69],"common":[70,128],"semantics,":[71],"while":[72],"other":[73],"aspects":[74],"remain":[75],"modality-specific.":[76],"This":[77],"insight":[78],"motivates":[79],"a":[80,91,96,154,159],"unified":[81],"multimodal":[82,185],"framework":[83],"joint":[85],"2D-3D":[86],"semantic":[87,103,126,165],"segmentation.":[88],"We":[89],"combine":[90],"SAM-based":[92],"vision":[93],"encoder":[94,99],"with":[95],"SPTNet-based":[97],"to":[100,129,141],"extract":[101],"complementary":[102],"representations.":[106],"The":[107,206],"resulting":[108],"both":[111,130,164],"modalities":[112],"explicitly":[114],"decomposed":[115],"into":[116,153],"shared":[117,123,151],"private":[119,134],"subspaces,":[120],"where":[121],"components":[124,135],"summarize":[125],"factors":[127],"domains,":[131],"preserve":[136],"properties":[137],"unique":[140],"each":[142],"modality.":[143],"A":[144],"lightweight":[145],"attention-based":[146],"fusion":[147],"module":[148],"aggregates":[149],"consistent":[155,178],"representation,":[157],"regularized":[160],"training":[161],"objective":[162],"ensures":[163],"subspace":[168],"independence.":[169],"Experiments":[170],"on":[171,194],"SemanticKITTI":[173],"nuScenes":[175,195],"benchmarks":[176],"demonstrate":[177],"improvements":[179],"accuracy":[182],"over":[183],"representative":[184],"baselines,":[186],"accompanied":[187],"competitive":[189],"computational":[190],"efficiency.":[191],"Cross-domain":[192],"evaluation":[193],"USA-Singapore":[196],"shows":[197],"performance":[199],"under":[200],"distribution":[201],"shifts,":[202],"demonstrating":[203],"strong":[204],"generalization.":[205],"implementation":[207],"code":[208],"publicly":[210],"available":[211],"at:":[212],"https://github.com/shuaizhang69/UniD-Shift.":[213]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-12T00:00:00"}