{"id":"https://openalex.org/W7130096378","doi":"https://doi.org/10.48550/arxiv.2602.14199","title":"Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation","display_name":"Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation","publication_year":2026,"publication_date":"2026-02-15","ids":{"openalex":"https://openalex.org/W7130096378","doi":"https://doi.org/10.48550/arxiv.2602.14199"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.14199","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14199","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":null,"license_id":null,"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.2602.14199","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126187140","display_name":"Hung Quoc Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nguyen, Hung","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102888876","display_name":"An T. Le","orcid":"https://orcid.org/0000-0003-0929-3316"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, An","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126174367","display_name":"Truong Quang Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Truong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126187140"],"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.46779999136924744,"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"}},"topics":[{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.46779999136924744,"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/T11019","display_name":"Image Enhancement Techniques","score":0.18199999630451202,"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":0.09229999780654907,"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/gaussian","display_name":"Gaussian","score":0.7091000080108643},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.565500020980835},{"id":"https://openalex.org/keywords/modulation","display_name":"Modulation (music)","score":0.39899998903274536},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.3458999991416931},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.34380000829696655},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3330000042915344},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.3271999955177307},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.32659998536109924}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7091000080108643},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6577000021934509},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5794000029563904},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4465999901294708},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3458999991416931},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.34380000829696655},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.32350000739097595},{"id":"https://openalex.org/C11930861","wikidata":"https://www.wikidata.org/wiki/Q181417","display_name":"Frequency modulation","level":3,"score":0.31709998846054077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C111350171","wikidata":"https://www.wikidata.org/wiki/Q7443700","display_name":"Second-generation wavelet transform","level":5,"score":0.29660001397132874},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29420000314712524},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2825999855995178},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.14199","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14199","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.14199","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14199","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":null,"license_id":null,"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":{"3D":[0,84],"Gaussian":[1,18,42,92,128,164],"Splatting":[2],"(3DGS)":[3],"has":[4],"emerged":[5],"as":[6,25],"a":[7,99,115,141],"powerful":[8],"approach":[9],"for":[10,105],"novel":[11],"view":[12],"synthesis.":[13],"However,":[14],"the":[15,46,50,57,75,110,134,148],"number":[16],"of":[17,49],"primitives":[19],"often":[20],"grows":[21],"substantially":[22],"during":[23,123],"training":[24],"finer":[26],"scene":[27],"details":[28],"are":[29],"reconstructed,":[30],"leading":[31],"to":[32,63],"increased":[33],"memory":[34],"and":[35,78],"storage":[36],"costs.":[37],"Recent":[38],"coarse-to-fine":[39],"strategies":[40],"regulate":[41],"growth":[43],"by":[44,74],"modulating":[45],"frequency":[47,66,102],"content":[48],"ground-truth":[51],"images.":[52],"In":[53,94],"particular,":[54],"AutoOpti3DGS":[55],"employs":[56],"learnable":[58],"Discrete":[59],"Wavelet":[60],"Transform":[61],"(DWT)":[62],"enable":[64],"data-adaptive":[65],"modulation.":[67],"Nevertheless,":[68],"its":[69],"modulation":[70,103,135],"depth":[71],"is":[72],"limited":[73],"1-level":[76],"DWT,":[77],"jointly":[79],"optimizing":[80],"wavelet":[81],"regularization":[82],"with":[83],"reconstruction":[85],"introduces":[86],"gradient":[87],"competition":[88],"that":[89,118,133,159],"promotes":[90],"excessive":[91],"densification.":[93],"this":[95],"paper,":[96],"we":[97,113,131],"propose":[98],"multi-level":[100],"DWT-based":[101],"framework":[104],"3DGS.":[106],"By":[107],"recursively":[108],"decomposing":[109],"low-frequency":[111],"subband,":[112],"construct":[114],"deeper":[116],"curriculum":[117],"provides":[119],"progressively":[120],"coarser":[121],"supervision":[122],"early":[124],"training,":[125],"consistently":[126],"reducing":[127],"counts.":[129],"Furthermore,":[130],"show":[132],"can":[136],"be":[137],"performed":[138],"using":[139],"only":[140],"single":[142],"scaling":[143],"parameter,":[144],"rather":[145],"than":[146],"learning":[147],"full":[149],"2-tap":[150],"high-pass":[151],"filter.":[152],"Experimental":[153],"results":[154],"on":[155],"standard":[156],"benchmarks":[157],"demonstrate":[158],"our":[160],"method":[161],"further":[162],"reduces":[163],"counts":[165],"while":[166],"maintaining":[167],"competitive":[168],"rendering":[169],"quality.":[170]},"counts_by_year":[],"updated_date":"2026-02-18T06:25:47.457606","created_date":"2026-02-18T00:00:00"}