{"id":"https://openalex.org/W4390494297","doi":"https://doi.org/10.1109/cisp-bmei60920.2023.10373386","title":"Implicit Neural Representation with Learnable Fourier Feature Encoding for View Synthesis","display_name":"Implicit Neural Representation with Learnable Fourier Feature Encoding for View Synthesis","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4390494297","doi":"https://doi.org/10.1109/cisp-bmei60920.2023.10373386"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei60920.2023.10373386","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373386","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5077028026","display_name":"Zhiyong Huo","orcid":"https://orcid.org/0000-0002-7192-2593"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiyong Huo","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China","College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101287134","display_name":"Quan Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Guo","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China","College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,College of Telecommunications and Information Engineering,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5077028026"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18318466,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9991000294685364,"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/computer-science","display_name":"Computer science","score":0.6665724515914917},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6477342247962952},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6391283273696899},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.6328148245811462},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6098273396492004},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5775456428527832},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5740382671356201},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5240511894226074},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49091821908950806},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49088722467422485},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4663587808609009},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.46242064237594604},{"id":"https://openalex.org/keywords/fourier-series","display_name":"Fourier series","score":0.4348589777946472},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.362551212310791},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.325800359249115},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23579320311546326}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6665724515914917},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6477342247962952},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6391283273696899},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.6328148245811462},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6098273396492004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5775456428527832},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5740382671356201},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5240511894226074},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49091821908950806},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49088722467422485},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4663587808609009},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.46242064237594604},{"id":"https://openalex.org/C207864730","wikidata":"https://www.wikidata.org/wiki/Q179467","display_name":"Fourier series","level":2,"score":0.4348589777946472},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.362551212310791},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.325800359249115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23579320311546326},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei60920.2023.10373386","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373386","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2963557767","https://openalex.org/W2963627347","https://openalex.org/W2963926543","https://openalex.org/W2964288609","https://openalex.org/W2971278627","https://openalex.org/W2981657250","https://openalex.org/W3035515538","https://openalex.org/W3035591705","https://openalex.org/W3036843665","https://openalex.org/W3092203888","https://openalex.org/W4200150166","https://openalex.org/W4287756134","https://openalex.org/W4288242633","https://openalex.org/W4293450691","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6755308174","https://openalex.org/W6763397881","https://openalex.org/W6763480078","https://openalex.org/W6775446277","https://openalex.org/W6780179280","https://openalex.org/W6781421651"],"related_works":["https://openalex.org/W2372020181","https://openalex.org/W2156531654","https://openalex.org/W1581723585","https://openalex.org/W4378714697","https://openalex.org/W2294330161","https://openalex.org/W2253069048","https://openalex.org/W2804553224","https://openalex.org/W140709781","https://openalex.org/W3214340375","https://openalex.org/W1510159504"],"abstract_inverted_index":{"The":[0],"implicit":[1],"neural":[2],"representation":[3],"uses":[4],"a":[5,11,15,49],"Multilayer":[6],"Perceptron":[7],"(MLP)":[8],"to":[9,23,66,77,102],"parameterize":[10],"3D":[12],"scene":[13],"as":[14],"continuous":[16],"function.":[17],"Synthetic":[18],"images":[19,83],"are":[20,87],"inaccurate":[21],"due":[22],"the":[24,46,68,75,95,125],"inherent":[25],"\"spectral":[26],"bias\"":[27],"of":[28,92,97,127],"MLPs,":[29],"which":[30],"preferentially":[31],"learn":[32,67],"low-frequency":[33],"information":[34,69,80],"during":[35],"network":[36,47],"training.":[37],"With":[38],"learnable":[39,113],"Fourier":[40,114],"Features,":[41],"we":[42],"encode":[43],"position":[44,53],"in":[45,48,55,71],"shift-invariant":[50],"manner,":[51],"capturing":[52],"relationships":[54],"multidimensional":[56],"space":[57],"effectively.":[58],"A":[59],"periodic":[60,119],"activation":[61,120],"function":[62],"is":[63,100],"also":[64],"introduced":[65],"contained":[70],"higher-order":[72],"derivatives,":[73],"allowing":[74],"MLP":[76],"extract":[78],"high-frequency":[79],"from":[81],"input":[82],"efficiently.":[84],"Simulation":[85],"experiments":[86,106],"conducted":[88],"on":[89,112],"two":[90],"types":[91],"datasets,":[93],"and":[94],"quality":[96,126],"view":[98,129],"synthesis":[99],"superior":[101],"previous":[103],"work.":[104],"Ablation":[105],"demonstrate":[107],"that":[108],"trainable":[109],"encoding":[110],"based":[111],"Feature":[115],"mapping":[116],"combined":[117],"with":[118],"functions":[121],"can":[122],"significantly":[123],"improve":[124],"novel":[128],"synthesis.":[130]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
