{"id":"https://openalex.org/W7155407095","doi":"https://doi.org/10.48550/arxiv.2604.20046","title":"Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training","display_name":"Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155407095","doi":"https://doi.org/10.48550/arxiv.2604.20046"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20046","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.2604.20046","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134448554","display_name":"Yangming Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhang, Yangming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134387406","display_name":"Jian Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134383646","display_name":"Kunxiong Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chaojian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134403277","display_name":"Wei Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Kunxiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134463573","display_name":"Miao Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Agrawal, Gagan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agrawal, Gagan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao, Yang Katie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Yang Katie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Jian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lin, Yingyan Celine","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yingyan Celine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yin, Miao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Miao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5134448554"],"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":0.5972999930381775,"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":0.5972999930381775,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.120899997651577,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.07199999690055847,"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/rendering","display_name":"Rendering (computer graphics)","score":0.6549000144004822},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5989000201225281},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5357999801635742},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5008000135421753},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4350999891757965},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3824000060558319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8652999997138977},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6549000144004822},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5989000201225281},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5016999840736389},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5008000135421753},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4099999964237213},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3824000060558319},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C2781357197","wikidata":"https://www.wikidata.org/wiki/Q5757597","display_name":"High memory","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30140000581741333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28679999709129333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20046","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.2604.20046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20046","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":[0,18],"Gaussian":[1,19,118],"Splatting":[2],"(3DGS)":[3],"has":[4],"revolutionized":[5],"novel":[6],"view":[7],"synthesis":[8],"with":[9,115,171],"high-quality":[10],"rendering":[11,129],"through":[12,91],"continuous":[13],"aggregations":[14],"of":[15,17,66,106,112],"millions":[16],"primitives.":[20],"However,":[21],"it":[22],"suffers":[23],"from":[24],"a":[25,37,82,121],"substantial":[26],"memory":[27,59,77,124,144,178],"footprint,":[28],"particularly":[29],"during":[30],"training":[31,71,76,85,136,161,177],"due":[32],"to":[33,55,173],"uncontrolled":[34],"densification,":[35],"posing":[36],"critical":[38],"bottleneck":[39],"for":[40],"deployment":[41],"on":[42,138,162],"memory-constrained":[43],"edge":[44],"devices.":[45],"While":[46],"existing":[47,150],"methods":[48],"prune":[49],"redundant":[50],"Gaussians":[51,67,90,108],"post-training,":[52],"they":[53],"fail":[54],"address":[56],"the":[57,63,70,75,99,134,181],"peak":[58,176],"spikes":[60],"caused":[61],"by":[62],"abrupt":[64],"growth":[65,93],"early":[68],"in":[69],"process.":[72],"To":[73],"solve":[74],"consumption":[78,179],"problem,":[79],"we":[80],"propose":[81],"systematic":[83],"memory-bounded":[84],"framework":[86,101,137],"that":[87],"dynamically":[88],"optimizes":[89],"iterative":[92],"and":[94,109],"pruning.":[95],"In":[96],"other":[97],"words,":[98],"proposed":[100,135,155],"alternates":[102],"between":[103],"incremental":[104],"pruning":[105],"low-impact":[107],"strategic":[110],"growing":[111],"new":[113],"primitives":[114],"an":[116],"adaptive":[117],"compensation,":[119],"maintaining":[120],"near-constant":[122],"low":[123],"usage":[125],"while":[126],"progressively":[127],"refining":[128],"fidelity.":[130],"We":[131],"comprehensively":[132],"evaluate":[133],"various":[139],"real-world":[140],"datasets":[141],"under":[142],"strict":[143],"constraints,":[145],"showing":[146],"significant":[147],"improvements":[148],"over":[149],"state-of-the-art":[151],"methods.":[152],"Particularly,":[153],"our":[154],"method":[156],"practically":[157],"enables":[158],"memory-efficient":[159],"3DGS":[160],"NVIDIA":[163],"Jetson":[164],"AGX":[165],"Xavier,":[166],"achieving":[167],"similar":[168],"visual":[169],"quality":[170],"up":[172],"80%":[174],"lower":[175],"than":[180],"original":[182],"3DGS.":[183]},"counts_by_year":[],"updated_date":"2026-04-28T06:04:28.489925","created_date":"2026-04-24T00:00:00"}
