{"id":"https://openalex.org/W4415535444","doi":"https://doi.org/10.1145/3746027.3755481","title":"GraphSplat: Sparse-View Generalizable 3D Gaussian Splatting is Worth Graph of Nodes","display_name":"GraphSplat: Sparse-View Generalizable 3D Gaussian Splatting is Worth Graph of Nodes","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415535444","doi":"https://doi.org/10.1145/3746027.3755481"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746027.3755481","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746027.3755481","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114325233","display_name":"Zeyang Bai","orcid":"https://orcid.org/0009-0003-8776-6980"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyang Bai","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-8776-6980","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101941243","display_name":"Yunbiao Wang","orcid":"https://orcid.org/0000-0001-9648-6073"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunbiao Wang","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9648-6073","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047125796","display_name":"Dongbo Yu","orcid":"https://orcid.org/0009-0002-8306-1054"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongbo Yu","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-8306-1054","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076518693","display_name":"Jun Xiao","orcid":"https://orcid.org/0000-0002-1799-3948"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Xiao","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1799-3948","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056518775","display_name":"Lupeng Liu","orcid":"https://orcid.org/0000-0003-2517-7593"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lupeng Liu","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2517-7593","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"10190","last_page":"10199"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9993000030517578,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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"}}],"keywords":[{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5396000146865845},{"id":"https://openalex.org/keywords/ghosting","display_name":"Ghosting","score":0.5386000275611877},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47119998931884766},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.45660001039505005},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.42260000109672546},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4138000011444092},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3986000120639801},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.39100000262260437},{"id":"https://openalex.org/keywords/view-synthesis","display_name":"View synthesis","score":0.39010000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015000224113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6420999765396118},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5422999858856201},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C2780531524","wikidata":"https://www.wikidata.org/wiki/Q551540","display_name":"Ghosting","level":2,"score":0.5386000275611877},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4138000011444092},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3986000120639801},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.39010000228881836},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.385699987411499},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.35530000925064087},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.29280000925064087},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746027.3755481","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3746027.3755481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746027.3755481","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G972408355","display_name":null,"funder_award_id":"U21A20515, 62476262, 62206263, 62271467, 62306297, 62306296","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2085905957","https://openalex.org/W2133665775","https://openalex.org/W2165584048","https://openalex.org/W2955639361","https://openalex.org/W2962785568","https://openalex.org/W2962793285","https://openalex.org/W2963557767","https://openalex.org/W2964121744","https://openalex.org/W3036563849","https://openalex.org/W3109585842","https://openalex.org/W3111114371","https://openalex.org/W3159481202","https://openalex.org/W3176368002","https://openalex.org/W3183092879","https://openalex.org/W3204859697","https://openalex.org/W3216476011","https://openalex.org/W4312258500","https://openalex.org/W4312969460","https://openalex.org/W4320167334","https://openalex.org/W4385259380","https://openalex.org/W4385318467","https://openalex.org/W4385815503","https://openalex.org/W4385960790","https://openalex.org/W4386076472","https://openalex.org/W4388505297","https://openalex.org/W4390873987","https://openalex.org/W4391800855","https://openalex.org/W4396786671","https://openalex.org/W4397028793","https://openalex.org/W4399151733","https://openalex.org/W4400573504","https://openalex.org/W4402715971","https://openalex.org/W4402715988","https://openalex.org/W4402715996","https://openalex.org/W4402716243","https://openalex.org/W4402716405","https://openalex.org/W4402727821","https://openalex.org/W4402753396","https://openalex.org/W4402754177","https://openalex.org/W4402754254","https://openalex.org/W4402952317","https://openalex.org/W4403761012","https://openalex.org/W4403946970","https://openalex.org/W4404346342","https://openalex.org/W4404525549","https://openalex.org/W4404525697","https://openalex.org/W4404526636","https://openalex.org/W4404545830","https://openalex.org/W4404893196","https://openalex.org/W4405079016","https://openalex.org/W4409365106"],"related_works":[],"abstract_inverted_index":{"Generalizable":[0],"3D":[1,14],"Gaussian":[2],"Splatting":[3],"(G-3DGS)":[4],"has":[5],"recently":[6],"emerged":[7],"as":[8],"a":[9,25,46,50,73,89,99,129],"promising":[10],"solution":[11],"for":[12,28,76,116],"efficient":[13],"scene":[15],"representation":[16],"and":[17,39,85,110,139,147,155,173],"novel":[18,77],"view":[19,78],"synthesis.":[20],"However,":[21],"sparse-view":[22,179],"scenarios":[23],"pose":[24],"critical":[26],"challenge":[27],"accurate":[29],"depth":[30,123,141],"estimation.":[31],"In":[32,125],"such":[33],"cases,":[34],"viewpoint":[35],"overlaps":[36],"are":[37,42],"minimal,":[38],"many":[40],"regions":[41],"visible":[43],"from":[44],"only":[45],"single":[47],"view.":[48],"As":[49],"result,":[51],"reliable":[52],"multi-view":[53,118,140],"matching":[54],"is":[55,98],"unavailable":[56],"in":[57,170],"these":[58],"areas,":[59],"leading":[60],"to":[61,95,114,136],"significant":[62],"reconstruction":[63],"quality":[64],"degradation.":[65],"To":[66],"tackle":[67],"this":[68],"bottleneck,":[69],"we":[70,127],"propose":[71],"GraphSplat,":[72],"feed-forward":[74],"framework":[75],"synthesis":[79],"that":[80,159],"dynamically":[81],"incorporates":[82],"both":[83],"cross-view":[84],"monocular":[86,138],"cues":[87],"through":[88],"graph-based":[90],"feature":[91],"aggregation":[92],"strategy.":[93],"Central":[94],"our":[96],"approach":[97],"Multi-view":[100],"Aggregate":[101],"Graph":[102],"Attention":[103],"(MAGA)":[104],"mechanism,":[105],"which":[106],"adaptively":[107],"reweights":[108],"intra-view":[109],"inter-view":[111],"node":[112],"connections":[113],"compensate":[115],"unreliable":[117],"correspondences":[119],"with":[120,168],"robust":[121],"single-view":[122],"priors.":[124],"addition,":[126],"design":[128],"Hierarchical":[130],"Depth":[131],"Fusion":[132],"Estimator":[133],"(HDFE)":[134],"module":[135],"integrate":[137],"cues,":[142],"effectively":[143],"reducing":[144],"ghosting":[145],"artifacts":[146],"improving":[148],"geometric":[149],"consistency.":[150],"Extensive":[151],"evaluations":[152],"on":[153],"RealEstate10K":[154],"ACID":[156],"benchmarks":[157],"show":[158],"GraphSplat":[160],"achieves":[161],"competitive":[162],"performance":[163],"against":[164],"prior":[165],"SOTA":[166],"methods,":[167],"improvements":[169],"appearance":[171],"fidelity":[172],"cross-dataset":[174],"generalization":[175],"particularly":[176],"under":[177],"challenging":[178],"conditions.":[180]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-25T00:00:00"}