{"id":"https://openalex.org/W7140235410","doi":"https://doi.org/10.48550/arxiv.2603.20337","title":"High-fidelity Multi-view Normal Integration with Scale-encoded Neural Surface Representation","display_name":"High-fidelity Multi-view Normal Integration with Scale-encoded Neural Surface Representation","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7140235410","doi":"https://doi.org/10.48550/arxiv.2603.20337"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20337","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20337","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":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.2603.20337","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yang, Tongyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Tongyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Guo, Heng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Heng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Matsushita, Yasuyuki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matsushita, Yasuyuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Okura, Fumio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okura, Fumio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Luo, Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Fan, Xin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Xin","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":0.40689998865127563,"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":0.40689998865127563,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.24940000474452972,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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":0.17739999294281006,"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/intrinsics","display_name":"Intrinsics","score":0.703000009059906},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6689000129699707},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.6434000134468079},{"id":"https://openalex.org/keywords/normal","display_name":"Normal","score":0.6280999779701233},{"id":"https://openalex.org/keywords/surface-reconstruction","display_name":"Surface reconstruction","score":0.4959999918937683},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4587000012397766},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4564000070095062},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4487999975681305}],"concepts":[{"id":"https://openalex.org/C2908650547","wikidata":"https://www.wikidata.org/wiki/Q20999234","display_name":"Intrinsics","level":2,"score":0.703000009059906},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6689000129699707},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.6434000134468079},{"id":"https://openalex.org/C118732077","wikidata":"https://www.wikidata.org/wiki/Q273176","display_name":"Normal","level":3,"score":0.6280999779701233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6236000061035156},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5544000267982483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5396999716758728},{"id":"https://openalex.org/C20885615","wikidata":"https://www.wikidata.org/wiki/Q825595","display_name":"Surface reconstruction","level":3,"score":0.4959999918937683},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4587000012397766},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39079999923706055},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3422999978065491},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20337","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20337","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":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.2603.20337","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20337","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":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":{"Previous":[0],"multi-view":[1,50,160],"normal":[2,52,98,161],"integration":[3,162],"methods":[4],"typically":[5],"sample":[6],"a":[7,71,92,100,123],"single":[8],"ray":[9],"per":[10],"pixel,":[11,20],"without":[12],"considering":[13],"the":[14,27,32,40,56,62,78,83,138],"spatial":[15,93],"area":[16,81],"covered":[17],"by":[18],"each":[19,88,134],"which":[21],"varies":[22],"with":[23,91],"camera":[24],"intrinsics":[25],"and":[26,95],"camera-to-object":[28],"distance.":[29],"Consequently,":[30],"when":[31],"target":[33],"object":[34],"is":[35],"captured":[36,111],"at":[37,42,112,155],"different":[38],"distances,":[39,157],"normals":[41,110,153],"corresponding":[43],"pixels":[44],"may":[45],"differ":[46],"across":[47],"views.":[48],"This":[49],"surface":[51,74,109,119,150],"inconsistency":[53],"results":[54,142],"in":[55,61],"blurring":[57],"of":[58],"high-frequency":[59],"details":[60],"reconstructed":[63],"surface.":[64],"To":[65],"address":[66],"this":[67],"issue,":[68],"we":[69,121],"propose":[70],"scale-encoded":[72],"neural":[73,84],"representation":[75],"that":[76,127,144],"incorporates":[77],"pixel":[79],"coverage":[80],"into":[82],"representation.":[85],"By":[86],"associating":[87],"3D":[89],"point":[90],"scale":[94,132],"calculating":[96],"its":[97],"from":[99,152],"hybrid":[101],"grid-based":[102],"encoding,":[103],"our":[104,145],"method":[105],"effectively":[106],"represents":[107],"multi-scale":[108],"varying":[113,156],"distances.":[114],"Furthermore,":[115],"to":[116,133],"enable":[117],"scale-aware":[118],"reconstruction,":[120],"introduce":[122],"mesh":[124],"extraction":[125],"module":[126],"assigns":[128],"an":[129],"optimal":[130],"local":[131],"vertex":[135],"based":[136],"on":[137],"training":[139],"observations.":[140],"Experimental":[141],"demonstrate":[143],"approach":[146],"consistently":[147],"yields":[148],"high-fidelity":[149],"reconstruction":[151],"observed":[154],"outperforming":[158],"existing":[159],"methods.":[163]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-25T00:00:00"}
