{"id":"https://openalex.org/W2971298073","doi":"https://doi.org/10.1109/icip.2019.8803757","title":"2.5D Convolution for RGB-D Semantic Segmentation","display_name":"2.5D Convolution for RGB-D Semantic Segmentation","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2971298073","doi":"https://doi.org/10.1109/icip.2019.8803757","mag":"2971298073"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5017198291","display_name":"Yajie Xing","orcid":"https://orcid.org/0000-0002-1226-1529"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yajie Xing","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100456762","display_name":"Jingbo Wang","orcid":"https://orcid.org/0000-0001-9700-6262"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingbo Wang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101412833","display_name":"Xiaokang Chen","orcid":"https://orcid.org/0000-0002-6188-5821"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaokang Chen","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103205993","display_name":"Gang Zeng","orcid":"https://orcid.org/0000-0002-9575-4651"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Zeng","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5017198291"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.4293,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.91518114,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1410","last_page":"1414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9995999932289124,"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":0.9988999962806702,"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/rgb-color-model","display_name":"RGB color model","score":0.8370250463485718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.787581205368042},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7641406059265137},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7627788782119751},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7071493864059448},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6971443891525269},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6742432117462158},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6194571256637573},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6052719354629517},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46269139647483826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44853949546813965},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2373540699481964},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13979792594909668}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8370250463485718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.787581205368042},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7641406059265137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627788782119751},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7071493864059448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6971443891525269},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6742432117462158},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6194571256637573},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6052719354629517},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46269139647483826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44853949546813965},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2373540699481964},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13979792594909668},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1565402342","https://openalex.org/W1905829557","https://openalex.org/W1923184257","https://openalex.org/W1985238052","https://openalex.org/W2098883970","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2485522583","https://openalex.org/W2489780108","https://openalex.org/W2563705555","https://openalex.org/W2587989515","https://openalex.org/W2745846084","https://openalex.org/W2775906317","https://openalex.org/W2776622059","https://openalex.org/W2777686015","https://openalex.org/W2886802132","https://openalex.org/W2962802951","https://openalex.org/W2963896186","https://openalex.org/W2964309882","https://openalex.org/W6605121731","https://openalex.org/W6633727509","https://openalex.org/W6723169197","https://openalex.org/W6733387591","https://openalex.org/W6748481559","https://openalex.org/W6750283149"],"related_works":["https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W3034421924","https://openalex.org/W2982536526","https://openalex.org/W4386858688","https://openalex.org/W4380302312","https://openalex.org/W4390971171","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNN)":[3],"have":[4],"achieved":[5],"great":[6],"success":[7],"in":[8,36,65],"RGB":[9],"semantic":[10,98],"segmentation.":[11],"RGB-D":[12,34,97],"images":[13],"provide":[14],"additional":[15],"depth":[16],"information,":[17],"which":[18,43],"can":[19,58,86],"improve":[20],"segmentation":[21,99],"performance.":[22],"To":[23],"take":[24],"full":[25],"advantages":[26],"of":[27,107],"the":[28,105],"3D":[29,46,70],"geometry":[30],"relations":[31,62],"provided":[32],"by":[33,49],"images,":[35],"this":[37],"paper,":[38],"we":[39],"propose":[40],"2.5D":[41,56],"convolution,":[42],"mimics":[44],"one":[45],"convolution":[47,53,57,71],"kernel":[48],"several":[50],"masked":[51],"2D":[52,77],"kernels.":[54],"Our":[55],"effectively":[59],"process":[60],"spatial":[61],"between":[63],"pixels":[64,75],"a":[66],"manner":[67],"similar":[68],"to":[69],"while":[72],"still":[73],"sampling":[74],"on":[76,94],"plane,":[78],"and":[79,102],"thus":[80],"saves":[81],"computational":[82],"cost.":[83],"And":[84],"it":[85],"be":[87],"seamlessly":[88],"incorporated":[89],"into":[90],"pretrained":[91],"CNNs.":[92],"Experiments":[93],"two":[95],"challenging":[96],"benchmarks":[100],"NYUDv2":[101],"SUN-RGBD":[103],"validate":[104],"effectiveness":[106],"our":[108],"approach.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}