{"id":"https://openalex.org/W4387158314","doi":"https://doi.org/10.1145/3581783.3612356","title":"P2I-NET: Mapping Camera Pose to Image via Adversarial Learning for New View Synthesis in Real Indoor Environments","display_name":"P2I-NET: Mapping Camera Pose to Image via Adversarial Learning for New View Synthesis in Real Indoor Environments","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387158314","doi":"https://doi.org/10.1145/3581783.3612356"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612356","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2309.15526","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101464221","display_name":"Xujie Kang","orcid":"https://orcid.org/0000-0002-0352-4075"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xujie Kang","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-0352-4075","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004007577","display_name":"Kanglin Liu","orcid":"https://orcid.org/0000-0002-6293-5464"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kanglin Liu","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-6293-5464","affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101518904","display_name":"Jiang Duan","orcid":"https://orcid.org/0000-0001-6867-6937"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Duan","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Southwestern University of Finance and Econom, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-6867-6937","affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Southwestern University of Finance and Econom, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022214509","display_name":"Yuanhao Gong","orcid":"https://orcid.org/0000-0001-5702-1927"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhao Gong","raw_affiliation_strings":["College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5702-1927","affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114259459","display_name":"Guoping Qiu","orcid":"https://orcid.org/0000-0002-5877-5648"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoping Qiu","raw_affiliation_strings":["College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-5877-5648","affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2635","last_page":"2643"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9973000288009644,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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":0.74935382604599},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7485424280166626},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5993818640708923},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.583792507648468},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5732069611549377},{"id":"https://openalex.org/keywords/view-synthesis","display_name":"View synthesis","score":0.5447654724121094},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5201869010925293},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5100167989730835},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.465236097574234},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.46126678586006165},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45904678106307983},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4521992802619934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32294437289237976},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14431798458099365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.74935382604599},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7485424280166626},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5993818640708923},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.583792507648468},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5732069611549377},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.5447654724121094},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5201869010925293},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5100167989730835},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.465236097574234},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.46126678586006165},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45904678106307983},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4521992802619934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32294437289237976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14431798458099365},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"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":2,"locations":[{"id":"doi:10.1145/3581783.3612356","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2309.15526","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.15526","pdf_url":"https://arxiv.org/pdf/2309.15526","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2309.15526","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.15526","pdf_url":"https://arxiv.org/pdf/2309.15526","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6600000262260437}],"awards":[{"id":"https://openalex.org/G1003541801","display_name":null,"funder_award_id":"2021A1515011584","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G4623173173","display_name":null,"funder_award_id":"62271323","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5392543476","display_name":null,"funder_award_id":"2023A1515012956","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G5430980299","display_name":null,"funder_award_id":"U22B2035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1989476314","https://openalex.org/W2009422376","https://openalex.org/W2091019609","https://openalex.org/W2194775991","https://openalex.org/W2298992465","https://openalex.org/W2785678896","https://openalex.org/W2799209711","https://openalex.org/W2808492412","https://openalex.org/W2889582485","https://openalex.org/W2963038612","https://openalex.org/W2963926543","https://openalex.org/W2968257580","https://openalex.org/W2969485315","https://openalex.org/W2971225342","https://openalex.org/W2976164226","https://openalex.org/W2983597143","https://openalex.org/W2984210651","https://openalex.org/W3024211078","https://openalex.org/W3035426517","https://openalex.org/W3035591705","https://openalex.org/W3095682719","https://openalex.org/W3096831136","https://openalex.org/W3109585842","https://openalex.org/W3167294693","https://openalex.org/W3193917007","https://openalex.org/W3203570626","https://openalex.org/W4200150166","https://openalex.org/W4200313684","https://openalex.org/W4221151978","https://openalex.org/W4235375376","https://openalex.org/W4286484885","https://openalex.org/W4312933868","https://openalex.org/W4320831569","https://openalex.org/W4378450585"],"related_works":["https://openalex.org/W2611780867","https://openalex.org/W2731344982","https://openalex.org/W2123263858","https://openalex.org/W1491099440","https://openalex.org/W3127959533","https://openalex.org/W2073038808","https://openalex.org/W2912321008","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W1998607122"],"abstract_inverted_index":{"Given":[0],"a":[1,24,76,122,172,209,241],"new":[2,66,87,193,242],"6DoF":[3],"camera":[4,110,260],"pose":[5,21,111,146,169,261],"in":[6,69,127,159,185],"an":[7],"indoor":[8,72,199,245],"environment,":[9,73,117],"we":[10,74,219,239],"study":[11],"the":[12,17,61,70,86,90,95,99,102,106,109,116,142,145,148,154,161,166,186],"challenging":[13],"problem":[14],"of":[15,26,52,98,101,115,124,147,153,211],"predicting":[16],"view":[18,67,88,114,194],"from":[19,60,89],"that":[20,152,203,221],"based":[22,43,213],"on":[23,44,49,197],"set":[25],"reference":[27],"RGBD":[28,252],"views.":[29],"Existing":[30],"explicit":[31],"or":[32],"implicit":[33],"3D":[34],"geometry":[35],"construction":[36],"methods":[37],"are":[38,138],"computationally":[39],"expensive":[40],"while":[41,233],"those":[42],"learning":[45],"have":[46,190],"predominantly":[47],"focused":[48],"isolated":[50],"views":[51],"object":[53],"categories":[54],"with":[55],"regular":[56],"geometric":[57],"structure.":[58],"Differing":[59],"traditional":[62],"render-inpaint":[63],"approach":[64],"to":[65,83,180,225],"synthesis":[68,195],"real":[71,156,167,198],"propose":[75],"conditional":[77,96],"generative":[78],"adversarial":[79],"neural":[80,175],"network":[81,176],"(P2I-NET)":[82],"directly":[84],"predict":[85],"given":[91],"pose.":[92],"P2I-NET":[93,204,222],"learns":[94],"distribution":[97],"images":[100],"environment":[103,246],"for":[104,140],"establishing":[105],"correspondence":[107],"between":[108,144],"and":[112,118,130,151,165],"its":[113,128],"achieves":[119],"this":[120,183],"through":[121],"number":[123,210],"innovative":[125],"designs":[126],"architecture":[129],"training":[131],"lost":[132],"function.":[133],"Two":[134],"auxiliary":[135],"discriminator":[136],"constraints":[137],"introduced":[139,179],"enforcing":[141],"consistency":[143,184],"generated":[149],"image":[150,158],"corresponding":[155],"world":[157,168],"both":[160],"latent":[162],"feature":[163],"space":[164],"space.":[170,188],"Additionally":[171],"deep":[173],"convolutional":[174],"(CNN)":[177],"is":[178,223],"further":[181],"reinforce":[182],"pixel":[187],"We":[189],"performed":[191],"extensive":[192],"experiments":[196],"datasets.":[200],"Results":[201],"show":[202,220],"has":[205,258],"superior":[206],"performance":[207],"against":[208],"NeRF":[212],"strong":[214],"baseline":[215],"models.":[216],"In":[217],"particular,":[218],"40":[224],"100":[226],"times":[227],"faster":[228],"than":[229],"these":[230],"competitor":[231],"techniques":[232],"synthesising":[234],"similar":[235],"quality":[236],"images.":[237],"Furthermore,":[238],"contribute":[240],"publicly":[243],"available":[244],"dataset":[247],"containing":[248],"22":[249],"high":[250],"resolution":[251],"videos":[253],"where":[254],"each":[255],"frame":[256],"also":[257],"accurate":[259],"parameters.":[262]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2023-09-30T00:00:00"}