{"id":"https://openalex.org/W7131219202","doi":"https://doi.org/10.1109/vcip67698.2025.11396883","title":"FPS: A Novel Test View Selection Strategy for Gaussian Splatting Quality Evaluation","display_name":"FPS: A Novel Test View Selection Strategy for Gaussian Splatting Quality Evaluation","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7131219202","doi":"https://doi.org/10.1109/vcip67698.2025.11396883"},"language":null,"primary_location":{"id":"doi:10.1109/vcip67698.2025.11396883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005953865","display_name":"Xueshi Hou","orcid":"https://orcid.org/0000-0003-3083-7656"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xueshi Hou","raw_affiliation_strings":["Tencent Media Lab"],"affiliations":[{"raw_affiliation_string":"Tencent Media Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126673649","display_name":"Shan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Liu","raw_affiliation_strings":["Tencent Media Lab"],"affiliations":[{"raw_affiliation_string":"Tencent Media Lab","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005953865"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71727411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.1193000003695488,"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.1193000003695488,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.11060000211000443,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.057999998331069946,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/gaussian","display_name":"Gaussian","score":0.5778999924659729},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5407000184059143},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.44040000438690186},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.39070001244544983},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3643999993801117}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8307999968528748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5925999879837036},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5778999924659729},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5407000184059143},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4616999924182892},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44040000438690186},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.39070001244544983},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3345000147819519},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip67698.2025.11396883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.48202890157699585}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2064076387","https://openalex.org/W2087309216","https://openalex.org/W2133665775","https://openalex.org/W2471962767","https://openalex.org/W2738551266","https://openalex.org/W2962785568","https://openalex.org/W2964288609","https://openalex.org/W3215769467","https://openalex.org/W4200150166","https://openalex.org/W4297662257","https://openalex.org/W4385318467","https://openalex.org/W4386066457"],"related_works":[],"abstract_inverted_index":{"Current":[0],"quality":[1,35],"evaluations":[2],"of":[3,33,71,77,90,157],"3D":[4,93,158],"Gaussian":[5,94,121,159],"Splatting":[6,95,122,160],"typically":[7],"rely":[8],"on":[9,56,134],"fixed-interval":[10,165],"view":[11,17,42,52,64,166],"sampling":[12,167],"(e.g.,":[13],"selecting":[14],"every":[15],"8th":[16],"as":[18],"test":[19,25,63],"view)":[20],"to":[21,30,37,86],"split":[22,104],"training":[23],"and":[24,41,74,97,127,140,154,169],"datasets,":[26],"which":[27],"may":[28],"lead":[29],"biased":[31],"assessments":[32],"reconstruction":[34],"due":[36],"limited":[38],"spatial":[39,69],"coverage":[40,70],"diversity.":[43],"To":[44],"address":[45],"this":[46],"issue,":[47],"we":[48],"propose":[49],"a":[50,116,151,171],"novel":[51],"selection":[53,65,105],"strategy":[54,106],"based":[55],"Farthest":[57],"Point":[58],"Sampling":[59],"(FPS)":[60],"that":[61,146],"optimizes":[62],"by":[66,119],"maximizing":[67],"the":[68,75,83,87,91,102,108,163],"camera":[72],"positions":[73],"diversity":[76],"viewing":[78],"directions.":[79],"In":[80],"our":[81,98,147],"experiments,":[82],"Baseline":[84],"refers":[85],"official":[88],"implementation":[89],"classic":[92],"method,":[96],"FPS":[99],"method":[100,149],"modifies":[101],"train/test":[103],"within":[107],"same":[109],"framework":[110,173],"for":[111,174],"fair":[112],"comparison.":[113],"We":[114],"conduct":[115],"subjective":[117,129],"experiment":[118],"rendering":[120],"results":[123,144],"into":[124],"video":[125],"sequences":[126],"collecting":[128],"scores":[130],"from":[131],"16":[132],"viewers":[133],"10":[135],"scenes,":[136],"including":[137],"both":[138],"real-world":[139],"synthetic":[141],"datasets.":[142],"Experimental":[143],"demonstrate":[145],"FPS-based":[148],"provides":[150],"more":[152],"comprehensive":[153],"reliable":[155],"evaluation":[156],"quality,":[161],"outperforming":[162],"conventional":[164],"approach":[168],"establishing":[170],"robust":[172],"performance":[175],"assessment.":[176]},"counts_by_year":[],"updated_date":"2026-02-25T21:11:00.739837","created_date":"2026-02-25T00:00:00"}
