{"id":"https://openalex.org/W7131430426","doi":"https://doi.org/10.48550/arxiv.2602.20933","title":"Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting","display_name":"Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131430426","doi":"https://doi.org/10.48550/arxiv.2602.20933"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.20933","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20933","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"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":"https://doi.org/10.48550/arxiv.2602.20933","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048939503","display_name":"Shuangkang Fang","orcid":"https://orcid.org/0009-0001-1066-5896"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fang, Shuangkang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063942521","display_name":"I\u2010Chao Shen","orcid":"https://orcid.org/0000-0003-4201-3793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, I-Chao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041793389","display_name":"Xuanyang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xuanyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124146072","display_name":"Zesheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zesheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109713321","display_name":"Yufeng Wang Yufeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yufeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101630751","display_name":"Wei Ding","orcid":"https://orcid.org/0000-0002-6150-2632"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Wenrui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126809139","display_name":"Gang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Gang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126789729","display_name":"Takeo Igarashi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Igarashi, Takeo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5048939503"],"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/T11019","display_name":"Image Enhancement Techniques","score":0.3449000120162964,"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/T11019","display_name":"Image Enhancement Techniques","score":0.3449000120162964,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2029000073671341,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.1509000062942505,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8945000171661377},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7603999972343445},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6456999778747559},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4982999861240387},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4700999855995178},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/spherical-harmonics","display_name":"Spherical harmonics","score":0.383899986743927},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.36469998955726624}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8945000171661377},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7603999972343445},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6456999778747559},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388999819755554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.548799991607666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5329999923706055},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4700999855995178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C3768446","wikidata":"https://www.wikidata.org/wiki/Q877100","display_name":"Spherical harmonics","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37880000472068787},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C127934551","wikidata":"https://www.wikidata.org/wiki/Q1148098","display_name":"Harmonic","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34360000491142273},{"id":"https://openalex.org/C188414643","wikidata":"https://www.wikidata.org/wiki/Q3001183","display_name":"Harmonics","level":3,"score":0.34220001101493835},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3260999917984009},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26989999413490295},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.26440000534057617}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.20933","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20933","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.20933","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20933","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"3D":[1],"Gaussian":[2,15],"Splatting":[3],"(3DGS)":[4],"Dropout":[5,63,106,144],"methods":[6,41,145],"address":[7,54],"overfitting":[8,126],"under":[9],"sparse-view":[10],"conditions":[11],"by":[12,32,110],"randomly":[13,72,111],"nullifying":[14],"opacities.":[16],"However,":[17],"we":[18,57,103],"identify":[19],"a":[20,60],"neighbor":[21],"compensation":[22],"effect":[23],"in":[24,119],"these":[25,40,55],"approaches:":[26],"dropped":[27],"Gaussians":[28,68,75],"are":[29],"often":[30],"compensated":[31],"their":[33,81,161],"neighbors,":[34],"weakening":[35],"the":[36,43,93,105],"intended":[37],"regularization.":[38],"Moreover,":[39],"overlook":[42],"contribution":[44],"of":[45],"high-degree":[46],"spherical":[47],"harmonic":[48],"coefficients":[49],"(SH)":[50],"to":[51,95,107,115,159],"overfitting.":[52],"To":[53],"issues,":[56],"propose":[58],"DropAnSH-GS,":[59],"novel":[61],"anchor-based":[62],"strategy.":[64],"Rather":[65],"than":[66],"dropping":[67,112],"independently,":[69],"our":[70],"method":[71],"selects":[73],"certain":[74],"as":[76],"anchors":[77,90],"and":[78,91,127,150],"simultaneously":[79],"removes":[80],"spatial":[82],"neighbors.":[83],"This":[84,122],"effectively":[85],"disrupts":[86],"local":[87],"redundancies":[88],"near":[89],"encourages":[92],"model":[94,131],"learn":[96],"more":[97],"robust,":[98],"globally":[99],"informed":[100],"representations.":[101],"Furthermore,":[102],"extend":[104],"color":[108],"attributes":[109],"higher-degree":[113],"SH":[114,134],"concentrate":[116],"appearance":[117],"information":[118],"lower-degree":[120],"SH.":[121],"strategy":[123],"further":[124],"mitigates":[125],"enables":[128],"flexible":[129],"post-training":[130],"compression":[132],"via":[133],"truncation.":[135],"Experimental":[136],"results":[137],"demonstrate":[138],"that":[139],"DropAnSH-GS":[140],"substantially":[141],"outperforms":[142],"existing":[143],"with":[146],"negligible":[147],"computational":[148],"overhead,":[149],"can":[151],"be":[152],"readily":[153],"integrated":[154],"into":[155],"various":[156],"3DGS":[157],"variants":[158],"enhance":[160],"performances.":[162],"Project":[163],"Website:":[164],"https://sk-fun.fun/DropAnSH-GS":[165]},"counts_by_year":[],"updated_date":"2026-02-26T06:34:08.959763","created_date":"2026-02-26T00:00:00"}