{"id":"https://openalex.org/W4386794594","doi":"https://doi.org/10.48550/arxiv.2309.07390","title":"Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images","display_name":"Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images","publication_year":2023,"publication_date":"2023-09-14","ids":{"openalex":"https://openalex.org/W4386794594","doi":"https://doi.org/10.48550/arxiv.2309.07390"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2309.07390","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.07390","pdf_url":"https://arxiv.org/pdf/2309.07390","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2309.07390","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009447015","display_name":"Junyang Wu","orcid":"https://orcid.org/0009-0003-6010-0089"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wu, Junyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100824790","display_name":"Yun Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Yun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009447015"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9549000263214111,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9549000263214111,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9542999863624573,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9165999889373779,"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/overfitting","display_name":"Overfitting","score":0.8743917942047119},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7802301049232483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6764107942581177},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6625583171844482},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6266807913780212},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5284126996994019},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4366888999938965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4206598103046417},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3509480059146881},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33241474628448486}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8743917942047119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7802301049232483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6764107942581177},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6625583171844482},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6266807913780212},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5284126996994019},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4366888999938965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4206598103046417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3509480059146881},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33241474628448486}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2309.07390","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.07390","pdf_url":"https://arxiv.org/pdf/2309.07390","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2309.07390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2309.07390","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2309.07390","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.07390","pdf_url":"https://arxiv.org/pdf/2309.07390","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4386794594.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W3099765033","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2],"have":[3,197],"witnessed":[4],"depth":[5],"and":[6,73,79,130,164,176,180,209,225],"pose":[7],"estimation":[8],"framework":[9],"on":[10,171],"unannotated":[11],"datasets":[12,175],"as":[13,203],"a":[14,64,148,187],"effective":[15,205],"pathway":[16],"to":[17,27,33,51,82,112,126,142,152,157,213],"succeed":[18],"in":[19,48,137,219],"endoscopic":[20,45,71,155],"navigation.":[21],"Most":[22],"current":[23,87],"techniques":[24],"are":[25,169,211],"dedicated":[26],"developing":[28],"more":[29,215],"advanced":[30],"neural":[31,57,80,88,139,150,165],"networks":[32,140],"improve":[34,74,159,184],"the":[35,41,54,68,75,84,93,110,122,132,154,160,172,181,191],"accuracy.":[36],"However,":[37],"existing":[38],"methods":[39],"ignore":[40],"special":[42],"properties":[43,69],"of":[44,56,67,70,77,86,105,134],"images,":[46,156],"resulting":[47],"an":[49,204],"inability":[50],"fully":[52],"unleash":[53,83],"power":[55,85],"networks.":[58,89,166],"In":[59],"this":[60],"study,":[61],"we":[62,91,146,194],"conduct":[63],"detail":[65],"analysis":[66],"images":[72,78,163,193],"compatibility":[76,161],"networks,":[81],"First,":[90],"introcude":[92],"Mask":[94],"Image":[95],"Modelling":[96],"(MIM)":[97],"module,":[98],"which":[99,196],"inputs":[100],"partial":[101,117],"image":[102,107],"information":[103,115,129],"instead":[104],"complete":[106],"information,":[108],"allowing":[109],"network":[111,151,199],"recover":[113],"global":[114,128],"from":[116],"pixel":[118],"information.":[119],"This":[120],"enhances":[121],"network'":[123],"s":[124],"ability":[125],"perceive":[127],"alleviates":[131],"phenomenon":[133],"local":[135,143],"overfitting":[136],"convolutional":[138],"due":[141],"artifacts.":[144],"Second,":[145],"propose":[147],"lightweight":[149],"enhance":[153],"explicitly":[158],"between":[162],"Extensive":[167],"experiments":[168],"conducted":[170],"three":[173],"public":[174],"one":[177],"inhouse":[178],"dataset,":[179],"proposed":[182],"modules":[183],"baselines":[185],"by":[186],"large":[188],"margin.":[189],"Furthermore,":[190],"enhanced":[192],"proposed,":[195],"higher":[198],"compatibility,":[200],"can":[201],"serve":[202],"data":[206],"augmentation":[207],"method":[208],"they":[210],"able":[212],"extract":[214],"stable":[216],"feature":[217,221],"points":[218],"traditional":[220],"point":[222],"matching":[223],"tasks":[224],"achieve":[226],"outstanding":[227],"performance.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2023-09-16T00:00:00"}