{"id":"https://openalex.org/W3154190827","doi":null,"title":"Learning from 2D: Pixel-to-Point Knowledge Transfer for 3D Pretraining.","display_name":"Learning from 2D: Pixel-to-Point Knowledge Transfer for 3D Pretraining.","publication_year":2021,"publication_date":"2021-04-10","ids":{"openalex":"https://openalex.org/W3154190827","mag":"3154190827"},"language":"en","primary_location":{"id":"mag:3154190827","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2104.04687v1","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2104.04687v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090502979","display_name":"Yueh-Cheng Liu","orcid":"https://orcid.org/0000-0002-8053-9801","observed_orcids":["https://orcid.org/0000-0002-8053-9801"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yueh-Cheng Liu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8053-9801","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009312990","display_name":"Yu\u2013Kai Huang","orcid":"https://orcid.org/0000-0002-2049-7339","observed_orcids":["https://orcid.org/0000-0002-2049-7339"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu\u2010Kai Huang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2049-7339","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082683969","display_name":"Hung-Yueh Chiang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hung-Yueh Chiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034130957","display_name":"Hung-Ting Su","orcid":"https://orcid.org/0009-0007-5212-0927","observed_orcids":["https://orcid.org/0009-0007-5212-0927"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hung-Ting Su","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0007-5212-0927","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112524766","display_name":"Zhe Yu Liu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhe Yu Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008578273","display_name":"Chin-Tang Chen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chin-Tang Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ching-Yu Tseng","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ching-Yu Tseng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043898632","display_name":"Winston H. Hsu","orcid":"https://orcid.org/0000-0002-3330-0638","observed_orcids":["https://orcid.org/0000-0002-3330-0638"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Winston H. Hsu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3330-0638","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.41040000319480896,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.41040000319480896,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.36550000309944153,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.2190999984741211,"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/knowledge-transfer","display_name":"knowledge transfer","score":0.765999972820282},{"id":"https://openalex.org/keywords/backprojection","display_name":"back-projection","score":0.9079999923706055},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.4000000059604645},{"id":"https://openalex.org/keywords/annotation-cost-reduction","display_name":"annotation cost reduction","score":0.5350000262260437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8170839548110962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6344900131225586},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6164583563804626},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5929937362670898},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5487990975379944},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5112450122833252},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5025863647460938},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.4952484667301178},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48898470401763916},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.4720883071422577},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46530681848526},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4310424327850342},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4238150119781494},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.393271267414093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35125434398651123},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3082628548145294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07155060768127441},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3154190827","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2104.04687v1","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3154190827","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2104.04687v1","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W343636949","https://openalex.org/W1690739335","https://openalex.org/W1821462560","https://openalex.org/W1923184257","https://openalex.org/W1985238052","https://openalex.org/W2108598243","https://openalex.org/W2115579991","https://openalex.org/W2321533354","https://openalex.org/W2395611524","https://openalex.org/W2460657278","https://openalex.org/W2544224704","https://openalex.org/W2555618208","https://openalex.org/W2558661413","https://openalex.org/W2560609797","https://openalex.org/W2561238782","https://openalex.org/W2624503621","https://openalex.org/W2731516819","https://openalex.org/W2737258237","https://openalex.org/W2739879705","https://openalex.org/W2785325870","https://openalex.org/W2786945063","https://openalex.org/W2798991696","https://openalex.org/W2887997457","https://openalex.org/W2899771611","https://openalex.org/W2904332125","https://openalex.org/W2936864631","https://openalex.org/W2939201152","https://openalex.org/W2939384298","https://openalex.org/W2944828972","https://openalex.org/W2947713925","https://openalex.org/W2948012107","https://openalex.org/W2949517790","https://openalex.org/W2949650786","https://openalex.org/W2950493473","https://openalex.org/W2951168573","https://openalex.org/W2951874610","https://openalex.org/W2954258401","https://openalex.org/W2963140444","https://openalex.org/W2963341956","https://openalex.org/W2963400571","https://openalex.org/W2965753840","https://openalex.org/W2979750740","https://openalex.org/W2980568538","https://openalex.org/W2981794819","https://openalex.org/W2994536315","https://openalex.org/W3003352491","https://openalex.org/W3005680577","https://openalex.org/W3034459762","https://openalex.org/W3035060554","https://openalex.org/W3036982689","https://openalex.org/W3044310049","https://openalex.org/W3109154950","https://openalex.org/W4297808394"],"related_works":["https://openalex.org/W3186632151","https://openalex.org/W4379539679","https://openalex.org/W4402982024","https://openalex.org/W4386075705","https://openalex.org/W4312050049","https://openalex.org/W4221145169","https://openalex.org/W4312367717","https://openalex.org/W3166446055","https://openalex.org/W7133230951","https://openalex.org/W4403565965"],"abstract_inverted_index":{"Most":[0],"of":[1,13,101,140],"the":[2,11,38,45,50,56,62,72,77,85,98,117,131,138,145,164,172],"3D":[3,24,68,82,110,126,133,155,165,177],"networks":[4,30],"are":[5,144],"trained":[6,151],"from":[7,32],"scratch":[8],"owning":[9],"to":[10,42,61,75,83,96,147,153],"lack":[12],"large-scale":[14],"labeled":[15,127],"datasets.":[16,35],"In":[17],"this":[18],"paper,":[19],"we":[20,70,89,143],"present":[21],"a":[22,113],"novel":[23],"pretraining":[25,119],"method":[26],"by":[27,48],"leveraging":[28],"2D":[29,34,46,66,80,103,115,124,150,169],"learned":[31],"rich":[33],"We":[36],"propose":[37],"pixel-to-point":[39],"knowledge":[40,170],"transfer":[41,86],"effectively":[43],"utilize":[44],"information":[47],"mapping":[49],"pixel-level":[51],"and":[52,67,81],"point-level":[53],"features":[54,78],"into":[55],"same":[57],"embedding":[58],"space.":[59],"Due":[60],"heterogeneous":[63],"nature":[64],"between":[65,79],"networks,":[69],"introduce":[71],"back-projection":[73],"function":[74],"align":[76],"make":[84],"possible.":[87],"Additionally,":[88],"devise":[90],"an":[91],"upsampling":[92],"feature":[93,104],"projection":[94],"layer":[95],"increase":[97],"spatial":[99],"resolution":[100],"high-level":[102],"maps,":[105],"which":[106],"helps":[107],"learning":[108],"fine-grained":[109],"representations.":[111],"With":[112],"pretrained":[114,167],"network,":[116],"proposed":[118],"process":[120],"requires":[121],"no":[122],"additional":[123],"or":[125],"data,":[128],"further":[129],"alleviating":[130],"expansive":[132],"data":[134],"annotation":[135],"cost.":[136],"To":[137],"best":[139],"our":[141],"knowledge,":[142],"first":[146],"exploit":[148],"existing":[149],"weights":[152],"pretrain":[154],"deep":[156],"neural":[157],"networks.":[158],"Our":[159],"intensive":[160],"experiments":[161],"show":[162],"that":[163],"models":[166],"with":[168],"boost":[171],"performances":[173],"across":[174],"various":[175],"real-world":[176],"downstream":[178],"tasks.":[179]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-04-26T00:00:00"}
