{"id":"https://openalex.org/W4386597231","doi":"https://doi.org/10.1109/icip49359.2023.10221958","title":"Learning Spatial-Temporal Embeddings for Sequential Point Cloud Frame Interpolation","display_name":"Learning Spatial-Temporal Embeddings for Sequential Point Cloud Frame Interpolation","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386597231","doi":"https://doi.org/10.1109/icip49359.2023.10221958"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10221958","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10221958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021558189","display_name":"Lili Zhao","orcid":"https://orcid.org/0000-0002-5182-7230"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lili Zhao","raw_affiliation_strings":["China Mobile Research Institute,Beijing,China","China Mobile Research Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute,Beijing,China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101238179","display_name":"Zhuoqun Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoqun Sun","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China","University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112998092","display_name":"Lancao Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lancao Ren","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China","University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103272302","display_name":"Qian Yin","orcid":"https://orcid.org/0000-0002-9448-1639"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Yin","raw_affiliation_strings":["Peking University,Beijing,China","Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University,Beijing,China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016117169","display_name":"Lei Yang","orcid":"https://orcid.org/0000-0001-9137-5235"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Yang","raw_affiliation_strings":["China Mobile Research Institute,Beijing,China","China Mobile Research Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute,Beijing,China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101544343","display_name":"Meng Guo","orcid":"https://orcid.org/0009-0003-3506-0636"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Guo","raw_affiliation_strings":["China Mobile Research Institute,Beijing,China","China Mobile Research Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute,Beijing,China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5021558189"],"corresponding_institution_ids":["https://openalex.org/I180662265"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12772117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"810","last_page":"814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9997000098228455,"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":0.9997000098228455,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9991000294685364,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9988999962806702,"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/computer-science","display_name":"Computer science","score":0.7837425470352173},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7624030113220215},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6847819685935974},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6811806559562683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6201431751251221},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6174267530441284},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5329645276069641},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.5075536966323853},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49531570076942444},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4701552391052246},{"id":"https://openalex.org/keywords/key-frame","display_name":"Key frame","score":0.4629669189453125},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4242267906665802},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.1971401572227478},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11025351285934448},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08728155493736267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7837425470352173},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7624030113220215},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6847819685935974},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6811806559562683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6201431751251221},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6174267530441284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5329645276069641},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.5075536966323853},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49531570076942444},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4701552391052246},{"id":"https://openalex.org/C2780139006","wikidata":"https://www.wikidata.org/wiki/Q1493902","display_name":"Key frame","level":3,"score":0.4629669189453125},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4242267906665802},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.1971401572227478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11025351285934448},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08728155493736267},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10221958","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10221958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1795280187","https://openalex.org/W2122578066","https://openalex.org/W2125101937","https://openalex.org/W2560722161","https://openalex.org/W2963390820","https://openalex.org/W2979750740","https://openalex.org/W2997958396","https://openalex.org/W3116580289","https://openalex.org/W3201672423","https://openalex.org/W3210254606","https://openalex.org/W4224917843","https://openalex.org/W4225300641","https://openalex.org/W4226021377","https://openalex.org/W4308067953","https://openalex.org/W4312284102","https://openalex.org/W4312896435","https://openalex.org/W6739778489","https://openalex.org/W6803177604"],"related_works":["https://openalex.org/W4389574804","https://openalex.org/W2394005538","https://openalex.org/W3016928466","https://openalex.org/W2936725271","https://openalex.org/W855007925","https://openalex.org/W2977517636","https://openalex.org/W2618671746","https://openalex.org/W2898107007","https://openalex.org/W1969179582","https://openalex.org/W2810129309"],"abstract_inverted_index":{"A":[0],"point":[1,37,53,76],"cloud":[2,38,54],"sequence":[3,55],"is":[4,66,96],"usually":[5],"acquired":[6,52],"at":[7],"a":[8,36,91,136,144],"low":[9],"frame":[10,39,48,93,148],"rate":[11,49],"owing":[12],"to":[13,45,73],"the":[14,17,21,25,47,51,61,101,114,129,161],"limitations":[15],"from":[16,149],"sensing":[18],"equipment.":[19],"Consequently,":[20],"immersive":[22],"experience":[23],"of":[24,50,109],"virtual":[26],"reality":[27],"might":[28],"be":[29,43],"greatly":[30],"degraded.":[31],"To":[32],"tackle":[33],"this":[34,89],"issue,":[35],"interpolation":[40,94],"process":[41],"can":[42],"used":[44],"increase":[46],"by":[56],"generating":[57],"new":[58],"frames":[59],"between":[60],"consecutive":[62],"ones.":[63],"However,":[64],"it":[65],"still":[67],"challenging":[68],"for":[69,79],"deep":[70],"neural":[71],"networks":[72],"synthesize":[74],"high-fidelity":[75],"clouds,":[77],"especially":[78],"those":[80],"with":[81],"complex":[82],"geometric":[83],"details":[84],"and":[85,104,120,132],"large":[86],"motion.":[87],"In":[88],"paper,":[90],"novel":[92],"network":[95],"proposed,":[97],"which":[98],"jointly":[99],"exploits":[100],"spatial":[102,130],"features":[103,131],"flows.":[105],"The":[106,125,141],"key":[107],"success":[108],"our":[110,157],"method":[111,158],"lies":[112],"in":[113,164],"developed":[115],"spatial-temporal":[116,137],"feature":[117,138],"propagation":[118],"module":[119],"temporal-aware":[121],"feature-to-point":[122],"mapping":[123],"module.":[124],"former":[126],"effectively":[127],"embeds":[128],"scene":[133],"flows":[134],"into":[135],"representation":[139],"(STFR).":[140],"latter":[142],"generates":[143],"much":[145],"improved":[146],"target":[147],"STFR.":[150],"Extensive":[151],"experimental":[152],"results":[153],"have":[154],"demonstrated":[155],"that":[156],"has":[159],"achieved":[160],"best":[162],"performance":[163],"most":[165],"cases.":[166]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
