{"id":"https://openalex.org/W1899309388","doi":"https://doi.org/10.1109/cvpr.2015.7298652","title":"Designing deep networks for surface normal estimation","display_name":"Designing deep networks for surface normal estimation","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1899309388","doi":"https://doi.org/10.1109/cvpr.2015.7298652","mag":"1899309388"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"preprint","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/A5100424261","display_name":"Xiaolong Wang","orcid":"https://orcid.org/0000-0002-5061-2529"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaolong Wang","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University","Robotics Institute, Carnegie Mellon University, USA"],"affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035759008","display_name":"David F. Fouhey","orcid":"https://orcid.org/0000-0001-5028-5161"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David F. Fouhey","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University","Robotics Institute, Carnegie Mellon University, USA"],"affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101761266","display_name":"Abhinav Gupta","orcid":"https://orcid.org/0000-0002-3646-2421"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Gupta","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University","Robotics Institute, Carnegie Mellon University, USA"],"affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100424261"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":23.772,"has_fulltext":false,"cited_by_count":346,"citation_normalized_percentile":{"value":0.99604315,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"547"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9987999796867371,"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":0.9983999729156494,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8114006519317627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7505441904067993},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6863846182823181},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6762422919273376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6598916053771973},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6025741696357727},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.579525887966156},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4800620973110199},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47249236702919006},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.45847585797309875},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4423847794532776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44003427028656006},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4307501018047333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3527374863624573},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3406989574432373},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17194506525993347},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10755661129951477},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10035297274589539}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8114006519317627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505441904067993},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6863846182823181},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6762422919273376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6598916053771973},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6025741696357727},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.579525887966156},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4800620973110199},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47249236702919006},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.45847585797309875},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4423847794532776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44003427028656006},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4307501018047333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3527374863624573},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3406989574432373},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17194506525993347},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10755661129951477},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10035297274589539},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2015.7298652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.763.1968","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.763.1968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1411.4958.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.828.5806","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.828.5806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Designing_Deep_Networks_2015_CVPR_paper.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8600000143051147,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W34885787","https://openalex.org/W125693051","https://openalex.org/W218762409","https://openalex.org/W337610345","https://openalex.org/W566730006","https://openalex.org/W1481823314","https://openalex.org/W1486646730","https://openalex.org/W1526242170","https://openalex.org/W1581592866","https://openalex.org/W1597474747","https://openalex.org/W1899309388","https://openalex.org/W1905829557","https://openalex.org/W1976970263","https://openalex.org/W2042119491","https://openalex.org/W2067912884","https://openalex.org/W2068397385","https://openalex.org/W2098883970","https://openalex.org/W2102605133","https://openalex.org/W2103380420","https://openalex.org/W2108598243","https://openalex.org/W2111736590","https://openalex.org/W2113107168","https://openalex.org/W2113325037","https://openalex.org/W2116851763","https://openalex.org/W2129259959","https://openalex.org/W2129587342","https://openalex.org/W2145567954","https://openalex.org/W2146814781","https://openalex.org/W2152380633","https://openalex.org/W2154579312","https://openalex.org/W2155871590","https://openalex.org/W2156406284","https://openalex.org/W2158211626","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2171740948","https://openalex.org/W2534523274","https://openalex.org/W2951234442","https://openalex.org/W2951713345","https://openalex.org/W2952623155","https://openalex.org/W2963542991","https://openalex.org/W6601424136","https://openalex.org/W6605121731","https://openalex.org/W6608795618","https://openalex.org/W6611422560","https://openalex.org/W6628796103","https://openalex.org/W6629070694","https://openalex.org/W6629368666","https://openalex.org/W6631696161","https://openalex.org/W6635091746","https://openalex.org/W6635490985","https://openalex.org/W6639633739","https://openalex.org/W6639961938","https://openalex.org/W6676297131","https://openalex.org/W6682751323","https://openalex.org/W6682761374","https://openalex.org/W6683067110","https://openalex.org/W6684191040","https://openalex.org/W6685261749","https://openalex.org/W6940791371"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"In":[0,17],"the":[1,24,37,45,61,85,91,111],"past":[2],"few":[3],"years,":[4],"convolutional":[5],"neural":[6],"nets":[7],"(CNN)":[8],"have":[9],"shown":[10],"incredible":[11],"promise":[12],"for":[13,23,60],"learning":[14],"visual":[15],"representations.":[16],"this":[18],"paper,":[19],"we":[20],"use":[21],"CNNs":[22],"task":[25,62],"of":[26,47,63,90,110],"predicting":[27],"surface":[28,64,95],"normals":[29],"from":[30],"a":[31,56],"single":[32],"image.":[33],"But":[34],"what":[35],"is":[36,104],"right":[38],"architecture?":[39],"We":[40,67,98],"propose":[41],"to":[42,54,88],"build":[43],"upon":[44],"decades":[46],"hard":[48],"work":[49],"in":[50,84],"3D":[51],"scene":[52],"understanding":[53],"design":[55],"new":[57],"CNN":[58],"architecture":[59,86],"normal":[65,96],"estimation.":[66,97],"show":[68,100,108],"that":[69,101],"incorporating":[70],"several":[71],"constraints":[72],"(man-made,":[73],"Manhattan":[74],"world)":[75],"and":[76,107],"meaningful":[77],"intermediate":[78],"representations":[79],"(room":[80],"layout,":[81],"edge":[82],"labels)":[83],"leads":[87],"state":[89,109],"art":[92,112],"performance":[93],"on":[94,114],"also":[99],"our":[102],"network":[103],"quite":[105],"robust":[106],"results":[113],"other":[115],"datasets":[116],"as":[117],"well":[118],"without":[119],"any":[120],"fine-tuning.":[121]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":29},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":47},{"year":2019,"cited_by_count":62},{"year":2018,"cited_by_count":47},{"year":2017,"cited_by_count":31},{"year":2016,"cited_by_count":40},{"year":2015,"cited_by_count":9}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}