{"id":"https://openalex.org/W3215781064","doi":"https://doi.org/10.1109/lra.2022.3155823","title":"Object-Aware Monocular Depth Prediction With Instance Convolutions","display_name":"Object-Aware Monocular Depth Prediction With Instance Convolutions","publication_year":2022,"publication_date":"2022-03-03","ids":{"openalex":"https://openalex.org/W3215781064","doi":"https://doi.org/10.1109/lra.2022.3155823","mag":"3215781064"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2022.3155823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2022.3155823","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"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":["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/A5055605941","display_name":"Enis Simsar","orcid":"https://orcid.org/0000-0002-6662-3249"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Enis Simsar","raw_affiliation_strings":["Technical University of Munich, M&#x00FC;nchen, Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, M&#x00FC;nchen, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057089169","display_name":"Evin P\u0131nar \u00d6rnek","orcid":"https://orcid.org/0000-0003-1023-2852"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Evin Pnar Ornek","raw_affiliation_strings":["Technical University of Munich, M&#x00FC;nchen, Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, M&#x00FC;nchen, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006401089","display_name":"Fabian Manhardt","orcid":"https://orcid.org/0000-0002-4577-4590"},"institutions":[{"id":"https://openalex.org/I4210132734","display_name":"Max Planck Computing and Data Facility","ror":"https://ror.org/03e21z229","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210132734","https://openalex.org/I4210139716"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Manhardt","raw_affiliation_strings":["Google Inc., Garching, Germany"],"affiliations":[{"raw_affiliation_string":"Google Inc., Garching, Germany","institution_ids":["https://openalex.org/I4210132734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039244167","display_name":"Helisa Dhamo","orcid":"https://orcid.org/0000-0003-1163-7448"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Helisa Dhamo","raw_affiliation_strings":["Technical University of Munich, M&#x00FC;nchen, Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, M&#x00FC;nchen, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046896448","display_name":"Nassir Navab","orcid":"https://orcid.org/0000-0002-6032-5611"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nassir Navab","raw_affiliation_strings":["Technical University of Munich, M&#x00FC;nchen, Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, M&#x00FC;nchen, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041092666","display_name":"Federico Tombari","orcid":"https://orcid.org/0000-0001-5598-5212"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Federico Tombari","raw_affiliation_strings":["Technical University of Munich, M&#x00FC;nchen, Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, M&#x00FC;nchen, Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5055605941"],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":null,"apc_paid":null,"fwci":0.1006,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.33220276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"7","issue":"2","first_page":"5389","last_page":"5396"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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":1.0,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9965999722480774,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7466174364089966},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7185174226760864},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7176583409309387},{"id":"https://openalex.org/keywords/classification-of-discontinuities","display_name":"Classification of discontinuities","score":0.7175491452217102},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.626691460609436},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5924985408782959},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5801992416381836},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5755640268325806},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5630438923835754},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5382897853851318},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5208195447921753},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.515193521976471},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3326079845428467},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3240150213241577},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21723663806915283},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08727160096168518}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7466174364089966},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7185174226760864},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7176583409309387},{"id":"https://openalex.org/C15627037","wikidata":"https://www.wikidata.org/wiki/Q541961","display_name":"Classification of discontinuities","level":2,"score":0.7175491452217102},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.626691460609436},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5924985408782959},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5801992416381836},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5755640268325806},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5630438923835754},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5382897853851318},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5208195447921753},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.515193521976471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3326079845428467},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3240150213241577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21723663806915283},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08727160096168518},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2022.3155823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2022.3155823","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1905829557","https://openalex.org/W1935978687","https://openalex.org/W2118246710","https://openalex.org/W2119531662","https://openalex.org/W2125416623","https://openalex.org/W2132947399","https://openalex.org/W2150066425","https://openalex.org/W2151996626","https://openalex.org/W2158211626","https://openalex.org/W2171740948","https://openalex.org/W2340897893","https://openalex.org/W2594519801","https://openalex.org/W2605938684","https://openalex.org/W2606794968","https://openalex.org/W2798365772","https://openalex.org/W2889002172","https://openalex.org/W2955639361","https://openalex.org/W2959581809","https://openalex.org/W2962807621","https://openalex.org/W2962960377","https://openalex.org/W2963000224","https://openalex.org/W2963033844","https://openalex.org/W2963125977","https://openalex.org/W2963150697","https://openalex.org/W2963264757","https://openalex.org/W2963316641","https://openalex.org/W2963446712","https://openalex.org/W2963488291","https://openalex.org/W2963591054","https://openalex.org/W2963677766","https://openalex.org/W2963710007","https://openalex.org/W2963870605","https://openalex.org/W2963911235","https://openalex.org/W2964912923","https://openalex.org/W2981542933","https://openalex.org/W2982763192","https://openalex.org/W2985299701","https://openalex.org/W2987860915","https://openalex.org/W2990946490","https://openalex.org/W3009020965","https://openalex.org/W3034275131","https://openalex.org/W3034528136","https://openalex.org/W3035358681","https://openalex.org/W3035679448","https://openalex.org/W3080631149","https://openalex.org/W3081167590","https://openalex.org/W3095980151","https://openalex.org/W3106873406","https://openalex.org/W3165495321","https://openalex.org/W3173727695","https://openalex.org/W3174581459","https://openalex.org/W3180199962","https://openalex.org/W3181256344","https://openalex.org/W3188511781","https://openalex.org/W4214520160","https://openalex.org/W4235111023","https://openalex.org/W6683067110","https://openalex.org/W6685261749","https://openalex.org/W6738279954","https://openalex.org/W6766261854"],"related_works":["https://openalex.org/W1972096828","https://openalex.org/W2529137940","https://openalex.org/W4302048708","https://openalex.org/W2359913921","https://openalex.org/W4205298958","https://openalex.org/W1981385315","https://openalex.org/W2486440955","https://openalex.org/W2050015493","https://openalex.org/W1595194509","https://openalex.org/W1996195943"],"abstract_inverted_index":{"With":[0],"the":[1,37,46,60,63,137,140,148,155,161],"advent":[2],"of":[3,18,22,101,118,139,157],"deep":[4],"learning,":[5],"estimating":[6,112,165],"depth":[7,38,114,166],"from":[8,28,54],"a":[9,16,89],"single":[10],"RGB":[11],"image":[12],"has":[13],"recently":[14],"received":[15],"lot":[17],"attention,":[19],"being":[20],"capable":[21],"empowering":[23],"many":[24],"different":[25,102],"applications":[26],"ranging":[27],"path":[29],"planning":[30],"for":[31],"robotics":[32],"to":[33,59,83,97,147],"computational":[34],"cinematography.":[35],"Nevertheless,while":[36],"maps":[39],"are":[40,51],"in":[41,73,81],"their":[42],"entirety":[43],"fairly":[44],"reliable,":[45],"estimates":[47],"around":[48,167],"object":[49,70,103,133],"discontinuities":[50],"still":[52],"far":[53],"satisfactory.":[55],"This":[56],"can":[57],"beattributed":[58],"fact":[61],"that":[62],"convolutional":[64,91,122],"operator":[65,92],"naturally":[66],"aggregates":[67],"features":[68],"across":[69],"discontinuities,":[71],"resulting":[72],"smooth":[74],"transitions":[75],"rather":[76],"than":[77],"clear":[78],"boundaries.":[79],"Therefore,":[80],"order":[82],"circumvent":[84],"this":[85],"issue,":[86],"we":[87,125],"propose":[88],"novel":[90],"which":[93,124],"is":[94,109,177],"explicitly":[95],"tailored":[96],"avoid":[98],"feature":[99],"aggregation":[100],"parts.":[104],"In":[105],"particular,":[106],"our":[107],"method":[108],"based":[110],"on":[111,136],"per-part":[113],"values":[115],"by":[116],"means":[117],"super-pixels.":[119,142],"The":[120],"proposed":[121],"operator,":[123],"dub":[126],"\u201cInstance":[127],"Convolution,\u201d":[128],"then":[129],"only":[130],"considers":[131],"each":[132],"part":[134],"individually":[135],"basis":[138],"estimated":[141],"Our":[143,175],"evaluation":[144],"with":[145],"respect":[146],"NYUv2,":[149],"iBims":[150],"and":[151],"KITTI":[152],"datasets":[153],"demonstrate":[154],"advantages":[156],"Instance":[158],"Convolutions":[159],"over":[160],"classical":[162],"convolution":[163],"at":[164,179],"occlusion":[168],"boundaries,":[169],"while":[170],"producing":[171],"comparable":[172],"results":[173],"elsewhere.":[174],"code":[176],"available":[178],"github.com/enisimsar/instance-conv.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
