{"id":"https://openalex.org/W4388963832","doi":"https://doi.org/10.48550/arxiv.2311.13533","title":"Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction","display_name":"Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction","publication_year":2023,"publication_date":"2023-11-22","ids":{"openalex":"https://openalex.org/W4388963832","doi":"https://doi.org/10.48550/arxiv.2311.13533"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2311.13533","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.13533","pdf_url":"https://arxiv.org/pdf/2311.13533","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,"listed_in":[],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2311.13533","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086591736","display_name":"Tam Thuc Do","orcid":"https://orcid.org/0000-0003-4632-0790","observed_orcids":["https://orcid.org/0000-0003-4632-0790"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Do, Tam Thuc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019574907","display_name":"Philip A. Chou","orcid":"https://orcid.org/0000-0002-7242-0210","observed_orcids":["https://orcid.org/0000-0002-7242-0210"]},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chou, Philip A.","raw_affiliation_strings":["York University , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University , Toronto , Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038897476","display_name":"Gene Cheung","orcid":"https://orcid.org/0000-0002-5571-4137","observed_orcids":["https://orcid.org/0000-0002-5571-4137"]},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Cheung, Gene","raw_affiliation_strings":["York University , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University , Toronto , Canada","institution_ids":["https://openalex.org/I192455969"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.8276000022888184,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.8276000022888184,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.09870000183582306,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.039400000125169754,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/3d-point-cloud-compression","display_name":"3D point cloud compression","score":0.8870000243186951},{"id":"https://openalex.org/keywords/b-spline-basis-functions","display_name":"B-spline basis functions","score":0.753000020980835},{"id":"https://openalex.org/keywords/coefficient-prediction","display_name":"coefficient prediction","score":0.7390000224113464},{"id":"https://openalex.org/keywords/graph-signal-processing","display_name":"graph signal processing","score":0.7789999842643738},{"id":"https://openalex.org/keywords/mpeg-7","display_name":"MPEG-7","score":0.36500000953674316},{"id":"https://openalex.org/keywords/point-cloud-reconstruction","display_name":"point cloud reconstruction","score":0.453000009059906}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7500008344650269},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5395841002464294},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4761081635951996},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.4705272912979126},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.4394466280937195},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4359327256679535},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4264480173587799},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40574556589126587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4001751244068146},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3111163377761841},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24520114064216614},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.17077171802520752},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.16336575150489807},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2311.13533","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.13533","pdf_url":"https://arxiv.org/pdf/2311.13533","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,"listed_in":[],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2311.13533","doi":"https://doi.org/10.48550/arxiv.2311.13533","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2311.13533","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,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2311.13533","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.13533","pdf_url":"https://arxiv.org/pdf/2311.13533","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,"listed_in":[],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388963832.pdf","grobid_xml":"https://content.openalex.org/works/W4388963832.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4388963825","https://openalex.org/W4362598199","https://openalex.org/W2926206832","https://openalex.org/W4417070622","https://openalex.org/W2893000317","https://openalex.org/W4398163632","https://openalex.org/W2997327074","https://openalex.org/W4224034487","https://openalex.org/W4304808261","https://openalex.org/W4387968111"],"abstract_inverted_index":{"We":[0,134],"extend":[1],"a":[2,14,18,74,106,115,122,140,148],"previous":[3],"study":[4],"on":[5,93],"3D":[6,45],"point":[7,152],"cloud":[8,153],"attribute":[9,21],"compression":[10],"scheme":[11],"that":[12,34,67,118],"uses":[13],"volumetric":[15,20],"approach:":[16],"given":[17,99],"target":[19],"function":[22,70],"$f":[23],":":[24],"\\mathbb{R}^3":[25],"\\mapsto":[26],"\\mathbb{R}$,":[27],"we":[28,91],"quantize":[29],"and":[30],"encode":[31],"parameters":[32,52,104],"$\u03b8$":[33,53],"characterize":[35],"$f$":[36],"at":[37,43,48,73],"the":[38,49,69,94,128],"encoder,":[39],"for":[40,86],"reconstruction":[41],"$f_{\\hat\u03b8}(\\mathbf(x))$":[42],"known":[44],"points":[46],"$\\mathbf(x)$":[47],"decoder.":[50],"Specifically,":[51],"are":[54,79],"quantized":[55],"coefficients":[56,98],"of":[57,96,105,151],"B-spline":[58],"basis":[59],"vectors":[60],"$\\mathbf\u03a6_l$":[61],"(for":[62],"order":[63],"$p":[64],"\\geq":[65],"2$)":[66],"span":[68],"space":[71],"$\\mathcal{F}_l^{(p)}$":[72],"particular":[75],"resolution":[76],"$l$,":[77],"which":[78],"coded":[80],"from":[81,111],"coarse":[82],"to":[83],"fine":[84],"resolutions":[85],"scalability.":[87],"In":[88],"this":[89],"work,":[90],"focus":[92],"prediction":[95],"finer-grained":[97],"coarser-grained":[100],"ones":[101],"by":[102,144],"learning":[103],"polynomial":[107],"bilateral":[108],"filter":[109,117],"(PBF)":[110],"data.":[112],"PBF":[113],"is":[114,119],"pseudo-linear":[116],"signal-dependent":[120],"with":[121],"graph":[123,129],"spectral":[124],"interpretation":[125],"common":[126],"in":[127],"signal":[130],"processing":[131],"(GSP)":[132],"field.":[133],"demonstrate":[135],"PBF's":[136],"predictive":[137],"performance":[138],"over":[139,147],"linear":[141],"predictor":[142],"inspired":[143],"MPEG":[145],"standardization":[146],"wide":[149],"range":[150],"datasets.":[154]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2023-11-24T00:00:00"}