{"id":"https://openalex.org/W4416269245","doi":"https://doi.org/10.1109/wacv61042.2026.00179","title":"3D Gaussian Point Encoders","display_name":"3D Gaussian Point Encoders","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W4416269245","doi":"https://doi.org/10.1109/wacv61042.2026.00179"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.04797","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064873455","display_name":"J.R. James","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jim James","raw_affiliation_strings":["Georgia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Tech","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087418259","display_name":"B.C. Wilson","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Wilson","raw_affiliation_strings":["Georgia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Tech","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055053078","display_name":"Simon Lucey","orcid":"https://orcid.org/0000-0002-6326-042X"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"The University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Simon Lucey","raw_affiliation_strings":["University of Adelaide"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Adelaide","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016775802","display_name":"James Hays","orcid":"https://orcid.org/0000-0001-7016-4252"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Hays","raw_affiliation_strings":["Georgia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Tech","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"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":"1788","last_page":"1797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7724000215530396,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7724000215530396,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.048900000751018524,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.013500000350177288,"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":0.7448999881744385},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6513000130653381},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.4961000084877014},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.46380001306533813},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.45019999146461487},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43130001425743103},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.40610000491142273},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.3747999966144562}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7448999881744385},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6513000130653381},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6047000288963318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.569100022315979},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.4961000084877014},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.45019999146461487},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43130001425743103},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.40610000491142273},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36000001430511475},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.3495999872684479},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33390000462532043},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32760000228881836},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.3010999858379364},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.29679998755455017},{"id":"https://openalex.org/C51267290","wikidata":"https://www.wikidata.org/wiki/Q5527848","display_name":"Gaussian random field","level":4,"score":0.28130000829696655},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/wacv61042.2026.00179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2511.04797","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.04797","pdf_url":"https://arxiv.org/pdf/2511.04797","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.04797","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.04797","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.04797","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.04797","pdf_url":"https://arxiv.org/pdf/2511.04797","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,117,162],"introduce":[4],"the":[5,94,105,164],"3D":[6,19,26,46,79,95,127,137,167,192],"Gaussian":[7,47,70,80,114,128,138,168,193],"Point":[8,81,129,169,194],"Encoder,":[9],"an":[10],"explicit":[11,22,112],"per-point":[12],"embedding":[13],"built":[14],"on":[15,60,203],"mixtures":[16],"of":[17,121,166],"learned":[18],"Gaussians.":[20],"This":[21],"geometric":[23],"representation":[24],"for":[25],"recognition":[27],"tasks":[28],"is":[29,42],"a":[30,69,148,172,181],"departure":[31],"from":[32,65,107,136],"widely":[33],"used":[34],"implicit":[35,108],"representations":[36],"such":[37],"as":[38,147,171],"PointNet.":[39],"However,":[40],"it":[41],"difficult":[43],"to":[44,67,111,125,140,199],"learn":[45],"encoders":[48,142],"in":[49,93,104,174,183],"end-to-end":[50],"fashion":[51],"with":[52],"standard":[53],"optimizers.":[54],"We":[55,132],"develop":[56],"optimization":[57],"techniques":[58,135],"based":[59],"natural":[61],"gradients":[62],"and":[63,85,157,179,185,189],"distillation":[64],"PointNets":[66],"find":[68],"Basis":[71],"that":[72,143],"can":[73,118],"reconstruct":[74],"PointNet":[75,151],"activations.":[76],"The":[77],"resulting":[78],"Encoders":[82,130,170,195],"are":[83,196],"faster":[84,146,178],"more":[86],"parameter":[87],"efficient":[88],"than":[89],"traditional":[90],"PointNets.":[91],"As":[92],"reconstruction":[96],"literature":[97],"where":[98],"there":[99],"has":[100],"been":[101],"considerable":[102],"interest":[103],"move":[106],"(e.g.,":[109,113],"NeRF)":[110],"Splatting)":[115],"representations,":[116],"take":[119],"advantage":[120],"computational":[122],"geometry":[123],"heuristics":[124],"accelerate":[126],"further.":[131],"extend":[133],"filtering":[134],"Splatting":[139],"construct":[141],"run":[144],"2.7\u00d7":[145],"comparable":[149],"accuracy":[150],"while":[152],"using":[153],"46%":[154],"less":[155],"memory":[156,184],"88%":[158],"fewer":[159],"FLOPs.":[160],"Furthermore,":[161],"demonstrate":[163],"effectiveness":[165],"component":[173],"Mamba3D,":[175],"running":[176],"1.27\u00d7":[177],"achieving":[180],"reduction":[182],"FLOPs":[186],"by":[187],"42%":[188],"54%":[190],"respectively.":[191],"lightweight":[197],"enough":[198],"achieve":[200],"high":[201],"framerates":[202],"CPU-only":[204],"devices.":[205]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-11-11T00:00:00"}