{"id":"https://openalex.org/W4220791965","doi":"https://doi.org/10.1007/s10994-022-06148-1","title":"SDANet: spatial deep attention-based for point cloud classification and segmentation","display_name":"SDANet: spatial deep attention-based for point cloud classification and segmentation","publication_year":2022,"publication_date":"2022-03-30","ids":{"openalex":"https://openalex.org/W4220791965","doi":"https://doi.org/10.1007/s10994-022-06148-1"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-022-06148-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06148-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06148-1.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06148-1.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073716643","display_name":"Jiangjiang Gao","orcid":"https://orcid.org/0000-0002-0047-3006"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiangjiang Gao","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China"],"raw_orcid":"https://orcid.org/0000-0002-0047-3006","affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101943365","display_name":"Jinhui Lan","orcid":"https://orcid.org/0000-0003-3213-3847"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhui Lan","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057896332","display_name":"Bingxu Wang","orcid":"https://orcid.org/0000-0001-6338-8186"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingxu Wang","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101439185","display_name":"Feifan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feifan Li","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5073716643"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":{"value":3290,"currency":"USD","value_usd":3290},"apc_paid":null,"fwci":2.2776,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.87155697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"111","issue":"4","first_page":"1327","last_page":"1348"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8462122082710266},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.71776282787323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6774698495864868},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6428593397140503},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5211743712425232},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5126377940177917},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5091680884361267},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4883222281932831},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48735862970352173},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45368918776512146},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4472613036632538},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.424618661403656},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33648136258125305},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13811084628105164},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12310293316841125},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08456233143806458}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8462122082710266},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71776282787323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6774698495864868},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6428593397140503},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5211743712425232},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5126377940177917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5091680884361267},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4883222281932831},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48735862970352173},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45368918776512146},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4472613036632538},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.424618661403656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33648136258125305},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13811084628105164},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12310293316841125},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08456233143806458},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-022-06148-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06148-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06148-1.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10994-022-06148-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06148-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06148-1.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4220791965.pdf","grobid_xml":"https://content.openalex.org/works/W4220791965.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W2116341502","https://openalex.org/W2211722331","https://openalex.org/W2460657278","https://openalex.org/W2553307952","https://openalex.org/W2556802233","https://openalex.org/W2624503621","https://openalex.org/W2737444374","https://openalex.org/W2775216572","https://openalex.org/W2798777114","https://openalex.org/W2798998662","https://openalex.org/W2799162093","https://openalex.org/W2908768031","https://openalex.org/W2910390795","https://openalex.org/W2950642167","https://openalex.org/W2951594382","https://openalex.org/W2962887844","https://openalex.org/W2963053547","https://openalex.org/W2968370607","https://openalex.org/W2968557240","https://openalex.org/W2981199548","https://openalex.org/W2985812748","https://openalex.org/W2989599677","https://openalex.org/W3012494314","https://openalex.org/W3025802147","https://openalex.org/W3035614624","https://openalex.org/W3037784355","https://openalex.org/W3040741167","https://openalex.org/W3042388945","https://openalex.org/W3046884570","https://openalex.org/W3111535274","https://openalex.org/W3125648170","https://openalex.org/W3153465022","https://openalex.org/W3202756173","https://openalex.org/W4285064070","https://openalex.org/W6640300118","https://openalex.org/W6747904511","https://openalex.org/W7026899033"],"related_works":["https://openalex.org/W4214536195","https://openalex.org/W2790662084","https://openalex.org/W2319888919","https://openalex.org/W3173326738","https://openalex.org/W2291847203","https://openalex.org/W3015465855","https://openalex.org/W2960184797","https://openalex.org/W2811390910","https://openalex.org/W3175684100","https://openalex.org/W2546942002"],"abstract_inverted_index":null,"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
