{"id":"https://openalex.org/W2890417831","doi":"https://doi.org/10.1109/lra.2019.2894913","title":"DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation From Stereo Imagery","display_name":"DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation From Stereo Imagery","publication_year":2019,"publication_date":"2019-01-24","ids":{"openalex":"https://openalex.org/W2890417831","doi":"https://doi.org/10.1109/lra.2019.2894913","mag":"2890417831"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2019.2894913","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2894913","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1809.04734","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Junming Zhang","orcid":"https://orcid.org/0000-0002-9603-0081"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junming Zhang","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0002-9603-0081","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Katherine A. Skinner","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Katherine A. Skinner","raw_affiliation_strings":["Robotics Program, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Program, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ram Vasudevan","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ram Vasudevan","raw_affiliation_strings":["Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":null,"display_name":"Matthew Johnson-Roberson","orcid":"https://orcid.org/0000-0002-0506-907X"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Johnson-Roberson","raw_affiliation_strings":["Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0002-0506-907X","affiliations":[{"raw_affiliation_string":"Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":3.5246,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.93550896,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"4","issue":"2","first_page":"1162","last_page":"1169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9298999905586243,"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":0.9298999905586243,"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":0.010099999606609344,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.007799999788403511,"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/semantics","display_name":"Semantics (computer science)","score":0.7016000151634216},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6779999732971191},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6590999960899353},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6572999954223633},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6291999816894531},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.5440000295639038},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4832000136375427},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3944000005722046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8098000288009644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7556999921798706},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.7016000151634216},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6779999732971191},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6590999960899353},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6572999954223633},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6291999816894531},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41839998960494995},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3765999972820282},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3483999967575073},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3456999957561493},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lra.2019.2894913","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2894913","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1809.04734","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.04734","pdf_url":"https://arxiv.org/pdf/1809.04734","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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1809.04734","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.04734","pdf_url":"https://arxiv.org/pdf/1809.04734","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320307103","display_name":"Ford Motor Company","ror":"https://ror.org/00g2tkw06"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1921093919","https://openalex.org/W1932937519","https://openalex.org/W2077545269","https://openalex.org/W2085854288","https://openalex.org/W2117248802","https://openalex.org/W2130951666","https://openalex.org/W2149184035","https://openalex.org/W2150066425","https://openalex.org/W2194775991","https://openalex.org/W2259424905","https://openalex.org/W2340897893","https://openalex.org/W2440384215","https://openalex.org/W2520707372","https://openalex.org/W2560023338","https://openalex.org/W2560474170","https://openalex.org/W2604231069","https://openalex.org/W2618530766","https://openalex.org/W2776033207","https://openalex.org/W2963231581","https://openalex.org/W2964002255","https://openalex.org/W6638039622","https://openalex.org/W6639102338","https://openalex.org/W6697658144","https://openalex.org/W6743438356","https://openalex.org/W6749483228","https://openalex.org/W6749819919","https://openalex.org/W6753133385","https://openalex.org/W6754156300"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"work":[1],"has":[2,37],"shown":[3,38],"that":[4,52,151,195,203],"convolutional":[5],"neural":[6],"networks":[7],"(CNNs)":[8],"can":[9,198],"be":[10],"applied":[11],"successfully":[12],"in":[13,23,41,78,123,170],"disparity":[14,64,98,159,214],"estimation,":[15],"but":[16],"these":[17,54,80],"methods":[18],"still":[19],"suffer":[20],"from":[21,106,126,139,207],"errors":[22],"regions":[24],"of":[25,63,69,89,111,129,146,157,167,185,213],"low":[26],"texture,":[27],"occlusions,":[28],"and":[29,61,135,161,191,202],"reflections.":[30],"Concurrently,":[31],"deep":[32],"learning":[33],"for":[34],"semantic":[35,70,108,162,208],"segmentation":[36,109,209],"great":[39,168],"progress":[40],"recent":[42],"years.":[43],"In":[44],"this":[45,90],"letter,":[46],"we":[47,73],"design":[48],"a":[49,75,152],"CNN":[50],"architecture":[51],"combines":[53],"two":[55,81],"tasks":[56,82],"to":[57],"improve":[58],"the":[59,67,93,107,112,130,140,147,183,211],"quality":[60],"accuracy":[62],"estimation":[65],"with":[66,102,174],"help":[68],"segmentation.":[71],"Specifically,":[72],"propose":[74],"network":[76,154],"structure":[77],"which":[79,124],"are":[83,100,133,166],"highly":[84],"coupled.":[85],"One":[86],"key":[87,144],"novelty":[88],"approach":[91,149],"is":[92,117,150,155],"two-stage":[94],"refinement":[95],"process.":[96],"Initial":[97],"estimates":[99,160],"refined":[101],"an":[103,120],"embedding":[104,205],"learned":[105,206],"branch":[110],"network.":[113],"The":[114],"proposed":[115,148],"model":[116,197],"trained":[118],"using":[119],"unsupervised":[121],"approach,":[122],"images":[125,138],"one":[127],"half":[128],"stereo":[131],"pair":[132],"warped":[134],"compared":[136],"against":[137],"other":[141],"camera.":[142],"Another":[143],"advantage":[145],"single":[153],"capable":[156],"outputting":[158],"labels.":[163],"These":[164],"outputs":[165],"use":[169],"autonomous":[171],"vehicle":[172],"operation;":[173],"real-time":[175],"constraints":[176],"being":[177],"key,":[178],"such":[179],"performance":[180,212],"improvements":[181],"increase":[182],"viability":[184],"driving":[186],"applications.":[187],"Experiments":[188],"on":[189],"KITTI":[190],"Cityscapes":[192],"datasets":[193],"show":[194],"our":[196],"achieve":[199],"state-of-the-art":[200],"results":[201],"leveraging":[204],"improves":[210],"estimation.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2018-09-27T00:00:00"}
