{"id":"https://openalex.org/W7168786622","doi":"https://doi.org/10.1145/3799902.3811221","title":"Object-Space Analysis of Local Contrast Sensitivity for Hierarchical Representations of 3D Gaussians","display_name":"Object-Space Analysis of Local Contrast Sensitivity for Hierarchical Representations of 3D Gaussians","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7168786622","doi":"https://doi.org/10.1145/3799902.3811221"},"language":null,"primary_location":{"id":"doi:10.1145/3799902.3811221","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799902.3811221","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 Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3799902.3811221","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140967855","display_name":"Naoto Yoshii","orcid":"https://orcid.org/0009-0002-5694-2710"},"institutions":[{"id":"https://openalex.org/I4400009020","display_name":"Institute of Science Tokyo","ror":"https://ror.org/05dqf9946","country_code":null,"type":"education","lineage":["https://openalex.org/I4400009020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naoto Yoshii","raw_affiliation_strings":["Institute of Science Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0002-5694-2710","affiliations":[{"raw_affiliation_string":"Institute of Science Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I4400009020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046001652","display_name":"Suguru Saito","orcid":"https://orcid.org/0000-0001-8635-3269"},"institutions":[{"id":"https://openalex.org/I4400009020","display_name":"Institute of Science Tokyo","ror":"https://ror.org/05dqf9946","country_code":null,"type":"education","lineage":["https://openalex.org/I4400009020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Suguru Saito","raw_affiliation_strings":["Institute of Science Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8635-3269","affiliations":[{"raw_affiliation_string":"Institute of Science Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I4400009020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002781077","display_name":"Masataka Sawayama","orcid":"https://orcid.org/0000-0003-0725-3214"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masataka Sawayama","raw_affiliation_strings":["Hokkaido University, Sapporo, Japan and Prometech CG Research, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0003-0725-3214","affiliations":[{"raw_affiliation_string":"Hokkaido University, Sapporo, Japan and Prometech CG Research, Tokyo, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014699738","display_name":"Yoshinori Dobashi","orcid":"https://orcid.org/0000-0002-2149-4113"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshinori Dobashi","raw_affiliation_strings":["Hokkaido University, Sapporo, Japan and Prometech CG Research, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2149-4113","affiliations":[{"raw_affiliation_string":"Hokkaido University, Sapporo, Japan and Prometech CG Research, Tokyo, Japan","institution_ids":["https://openalex.org/I205349734"]}]}],"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":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.7095999717712402},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6704000234603882},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.47350001335144043},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4187000095844269},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4106000065803528},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.37560001015663147},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3336000144481659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722100019454956},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.7095999717712402},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6704000234603882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6632000207901001},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5857999920845032},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.47350001335144043},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4519999921321869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3336000144481659},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.3280999958515167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.31369999051094055},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.2865999937057495},{"id":"https://openalex.org/C100921725","wikidata":"https://www.wikidata.org/wiki/Q1650811","display_name":"Spatial frequency","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.2840999960899353},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3799902.3811221","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799902.3811221","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 Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3799902.3811221","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799902.3811221","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 Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1999908130","https://openalex.org/W2014845093","https://openalex.org/W2018604504","https://openalex.org/W2034676897","https://openalex.org/W2118573764","https://openalex.org/W2123492407","https://openalex.org/W2133665775","https://openalex.org/W2146103513","https://openalex.org/W2471962767","https://openalex.org/W2738551266","https://openalex.org/W2941909038","https://openalex.org/W3097691005","https://openalex.org/W3109585842","https://openalex.org/W3185070214","https://openalex.org/W3215769467","https://openalex.org/W4247218844","https://openalex.org/W4286611186","https://openalex.org/W4313451221","https://openalex.org/W4385318467","https://openalex.org/W4397028793","https://openalex.org/W4402699188","https://openalex.org/W4402716301","https://openalex.org/W4402775760","https://openalex.org/W4407197278","https://openalex.org/W4408565100","https://openalex.org/W4409694085","https://openalex.org/W4410204460","https://openalex.org/W4410617756"],"related_works":[],"abstract_inverted_index":{"3D":[0,54],"Gaussian":[1,26,55,64,88,123],"Splatting":[2],"(3DGS)":[3],"has":[4],"recently":[5],"attracted":[6],"considerable":[7],"attention":[8],"as":[9],"an":[10,46,98],"efficient":[11],"representation":[12,113],"for":[13,34,53],"high-quality":[14],"novel-view":[15],"synthesis.":[16],"However,":[17],"3DGS":[18,112],"typically":[19],"relies":[20],"on":[21,132],"a":[22,110,116],"large":[23],"number":[24],"of":[25,50,63,76,87,100],"primitives,":[27],"making":[28],"perceptually":[29],"informed":[30],"level-of-detail":[31],"control":[32],"essential":[33],"balancing":[35],"visual":[36,141],"quality":[37,142],"and":[38,66,114],"rendering":[39,118,145],"efficiency.":[40],"In":[41],"this":[42,101],"paper,":[43],"we":[44,70,103],"present":[45],"object-space":[47],"analysis":[48],"framework":[49],"contrast":[51],"sensitivity":[52],"representations.":[56],"By":[57],"exploiting":[58],"the":[59,67,72,105,137],"analytical":[60],"Fourier":[61],"transform":[62],"primitives":[65,89],"projection\u2013slice":[68],"theorem,":[69],"estimate":[71],"spatial":[73],"frequency":[74],"response":[75],"projected":[77],"Gaussians":[78],"without":[79],"explicit":[80],"rasterization.":[81],"This":[82],"enables":[83],"direct":[84],"perceptual":[85,107],"assessment":[86,108],"in":[90],"object":[91],"space":[92],"under":[93],"given":[94],"viewing":[95],"conditions.":[96],"As":[97],"application":[99],"analysis,":[102],"integrate":[104],"proposed":[106,138],"into":[109],"hierarchical":[111],"realize":[115],"foveated":[117],"scheme":[119],"that":[120,136],"selects":[121],"appropriate":[122],"levels":[124],"at":[125],"runtime":[126],"using":[127],"simple":[128],"comparisons.":[129],"Experimental":[130],"results":[131],"real-world":[133],"scenes":[134],"demonstrate":[135],"method":[139],"preserves":[140],"while":[143],"reducing":[144],"cost":[146],"compared":[147],"to":[148],"full-quality":[149],"rendering.":[150]},"counts_by_year":[],"updated_date":"2026-07-17T05:58:32.477365","created_date":"2026-07-17T00:00:00"}
