{"id":"https://openalex.org/W4386075806","doi":"https://doi.org/10.1109/cvpr52729.2023.00487","title":"Trap Attention: Monocular Depth Estimation with Manual Traps","display_name":"Trap Attention: Monocular Depth Estimation with Manual Traps","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4386075806","doi":"https://doi.org/10.1109/cvpr52729.2023.00487"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52729.2023.00487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52729.2023.00487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-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/A5101607466","display_name":"Chao Ning","orcid":"https://orcid.org/0000-0001-8247-1700"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Ning","raw_affiliation_strings":["Northwestern Polytechnical University,Xi&#x0027;an,China,710072"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,Xi&#x0027;an,China,710072","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071986802","display_name":"Hongping Gan","orcid":"https://orcid.org/0000-0002-4853-5077"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongping Gan","raw_affiliation_strings":["Northwestern Polytechnical University,Xi&#x0027;an,China,710072"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,Xi&#x0027;an,China,710072","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101607466"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":2.2225,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.89979984,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5033","last_page":"5043"},"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":0.9990000128746033,"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/T11105","display_name":"Advanced Image Processing 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/computer-science","display_name":"Computer science","score":0.7425956726074219},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6907589435577393},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6243962049484253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6182988882064819},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5716033577919006},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5429731011390686},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5088232159614563},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4766336679458618},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.44318053126335144},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.423137903213501},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41187137365341187},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30238083004951477},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17603909969329834},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16911154985427856}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7425956726074219},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6907589435577393},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6243962049484253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6182988882064819},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5716033577919006},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5429731011390686},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5088232159614563},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4766336679458618},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.44318053126335144},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.423137903213501},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41187137365341187},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30238083004951477},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17603909969329834},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16911154985427856},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52729.2023.00487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52729.2023.00487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1983756589","display_name":null,"funder_award_id":"62101455","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1803059841","https://openalex.org/W1923184257","https://openalex.org/W2115579991","https://openalex.org/W2124907686","https://openalex.org/W2132400125","https://openalex.org/W2132947399","https://openalex.org/W2158211626","https://openalex.org/W2170213208","https://openalex.org/W2300779272","https://openalex.org/W2336968928","https://openalex.org/W2476548250","https://openalex.org/W2520707372","https://openalex.org/W2593414960","https://openalex.org/W2752782242","https://openalex.org/W2884585870","https://openalex.org/W2899663614","https://openalex.org/W2908510526","https://openalex.org/W2951234442","https://openalex.org/W2962741876","https://openalex.org/W2962960377","https://openalex.org/W2963091558","https://openalex.org/W2963488291","https://openalex.org/W2963591054","https://openalex.org/W2964912923","https://openalex.org/W2985775862","https://openalex.org/W2990946490","https://openalex.org/W3030520226","https://openalex.org/W3105431515","https://openalex.org/W3108179104","https://openalex.org/W3138516171","https://openalex.org/W3171206729","https://openalex.org/W3173727695","https://openalex.org/W3174688521","https://openalex.org/W3205856632","https://openalex.org/W4214520160","https://openalex.org/W4214634256","https://openalex.org/W4214651109","https://openalex.org/W4221155972","https://openalex.org/W4287022992","https://openalex.org/W4295312788","https://openalex.org/W4312460030","https://openalex.org/W4385245566","https://openalex.org/W6605121731","https://openalex.org/W6678569853","https://openalex.org/W6679911085","https://openalex.org/W6683067110","https://openalex.org/W6685261749","https://openalex.org/W6697658144","https://openalex.org/W6703874418","https://openalex.org/W6739901393","https://openalex.org/W6753412334","https://openalex.org/W6755977528","https://openalex.org/W6757817989","https://openalex.org/W6766261854","https://openalex.org/W6766978945","https://openalex.org/W6778485988","https://openalex.org/W6786385858","https://openalex.org/W6800217721","https://openalex.org/W6803916128","https://openalex.org/W6810227511"],"related_works":["https://openalex.org/W4224011692","https://openalex.org/W2113039159","https://openalex.org/W2094957557","https://openalex.org/W127013308","https://openalex.org/W2337415362","https://openalex.org/W4321512589","https://openalex.org/W4285740874","https://openalex.org/W1628937209","https://openalex.org/W3185738386","https://openalex.org/W4312857205"],"abstract_inverted_index":{"Predicting":[0],"a":[1,7,11,21,105,114,166],"high":[2,93],"quality":[3],"depth":[4,49,90,161,180,202],"map":[5,68],"from":[6,181],"single":[8,182],"image":[9,183],"is":[10,217],"challenging":[12],"task,":[13],"because":[14],"it":[15],"exists":[16],"infinite":[17],"possibility":[18],"to":[19,24,37,60,76,88,108,152,177],"project":[20],"2D":[22],"scene":[23],"the":[25,48,66,70,77,123,131,135,145,170,174,179,185,197],"corresponding":[26],"3D":[27],"scene.":[28],"Recently,":[29],"some":[30,120],"studies":[31],"introduced":[32],"multi-head":[33],"attention":[34,67,132,176],"(MHA)":[35],"modules":[36],"perform":[38],"long-range":[39,110],"interaction,":[40],"which":[41,118,164],"have":[42],"shown":[43],"significant":[44],"progress":[45],"in":[46,92,143,184,200],"regressing":[47],"maps.":[50],"The":[51],"main":[52],"functions":[53],"of":[54,80,139,214],"MHA":[55,87],"can":[56,84,149,195],"be":[57,150],"loosely":[58],"summarized":[59],"capture":[61],"long-distance":[62],"information":[63],"and":[64,112,129,172,208],"report":[65],"by":[69,134],"relationship":[71],"between":[72],"pixels.":[73],"However,":[74],"due":[75],"quadratic":[78,146],"complexity":[79,148],"MHA,":[81],"these":[82],"methods":[83,199],"not":[85],"leverage":[86],"compute":[89],"features":[91],"resolution":[94],"with":[95,210],"an":[96,158],"appropriate":[97],"computational":[98,147],"complexity.":[99],"In":[100],"this":[101],"paper,":[102],"we":[103,156],"exploit":[104],"depth-wise":[106],"convolution":[107,140],"obtain":[109],"information,":[111],"propose":[113],"novel":[115],"trap":[116,160,175],"attention,":[117],"sets":[119],"traps":[121],"on":[122,204],"extended":[124],"space":[125],"for":[126],"each":[127],"pixel,":[128],"forms":[130],"mechanism":[133],"feature":[136],"retention":[137],"ratio":[138],"window,":[141],"resulting":[142],"that":[144,191],"converted":[151],"linear":[153],"form.":[154],"Then":[155],"build":[157],"encoder-decoder":[159],"estimation":[162,203],"network,":[163],"introduces":[165],"vision":[167],"transformer":[168],"as":[169],"encoder,":[171],"uses":[173],"estimate":[178],"decoder.":[186],"Extensive":[187],"experimental":[188],"results":[189],"demonstrate":[190],"our":[192],"proposed":[193],"network":[194],"outperform":[196],"state-of-the-art":[198],"monocular":[201],"datasets":[205],"NYU":[206],"Depth-v2":[207],"KITTI,":[209],"significantly":[211],"reduced":[212],"number":[213],"parameters.":[215],"Code":[216],"available":[218],"at:":[219],"https://github.com/ICSResearch/TrapAttention.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
