{"id":"https://openalex.org/W4406267551","doi":"https://doi.org/10.1109/ist63414.2024.10759239","title":"PDAST: Paradigm Towards Weather Domain Adaptation for 3D Detection Based on Density Aware Pooling and Self-Training","display_name":"PDAST: Paradigm Towards Weather Domain Adaptation for 3D Detection Based on Density Aware Pooling and Self-Training","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4406267551","doi":"https://doi.org/10.1109/ist63414.2024.10759239"},"language":"en","primary_location":{"id":"doi:10.1109/ist63414.2024.10759239","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist63414.2024.10759239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5065844850","display_name":"Yonghui Huang","orcid":"https://orcid.org/0000-0002-9745-4177"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghui Huang","raw_affiliation_strings":["School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100688257","display_name":"Xi Zhang","orcid":"https://orcid.org/0000-0001-8909-2201"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Zhang","raw_affiliation_strings":["School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100609886","display_name":"Chuan Hu","orcid":"https://orcid.org/0000-0001-5379-1561"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Hu","raw_affiliation_strings":["School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101664660","display_name":"Yanhao Liu","orcid":"https://orcid.org/0009-0003-8706-5543"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanhao Liu","raw_affiliation_strings":["School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104242224","display_name":"Yakang Wang","orcid":"https://orcid.org/0009-0005-1083-2892"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yakang Wang","raw_affiliation_strings":["School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9829999804496765,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9829999804496765,"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"}},{"id":"https://openalex.org/T12983","display_name":"Satellite Image Processing and Photogrammetry","score":0.96670001745224,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9546999931335449,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.8656063079833984},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7771494388580322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7487192153930664},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6912230253219604},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6784378290176392},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5490062236785889},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5013809204101562},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4388096332550049},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38179317116737366},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08665671944618225},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.08664479851722717},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07683998346328735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07543247938156128}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.8656063079833984},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7771494388580322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487192153930664},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6912230253219604},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6784378290176392},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5490062236785889},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5013809204101562},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4388096332550049},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38179317116737366},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08665671944618225},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.08664479851722717},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07683998346328735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07543247938156128},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"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/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist63414.2024.10759239","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist63414.2024.10759239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.8500000238418579,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4394775207","https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W3203792196","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3156096827"],"abstract_inverted_index":{"Most":[0],"3D":[1,135,171],"detection":[2,136],"algorithms":[3],"are":[4,59],"developed":[5],"and":[6,50,83,101,140,149,173,226],"validated":[7],"on":[8,131,143,191,209],"datasets":[9,146],"collected":[10],"during":[11],"clear":[12,148],"weather,":[13,221],"while":[14],"performance":[15,157,196,216],"significantly":[16],"deteriorates":[17],"under":[18],"severe":[19,220],"weather":[20,37,68],"conditions":[21,69,200],"such":[22,222],"as":[23,106,223],"snowy":[24,67,150,210,224],"days.":[25],"To":[26],"address":[27],"this":[28,123],"problem,":[29],"we":[30,125],"propose":[31],"PDAST,":[32],"an":[33],"unsupervised":[34,198],"pipeline":[35],"for":[36,164,177,231],"domain":[38],"adaptation.":[39],"PDAST":[40,130,154,213],"involves":[41],"point":[42,88,92],"density":[43,120],"dealing":[44],"(PDD),":[45],"a":[46,51,109,132,156],"data":[47,77],"augmentation":[48,78],"method,":[49],"density-aware":[52],"pooling":[53],"module":[54,97],"(PDV),":[55],"both":[56],"of":[57,116,129,159,228],"which":[58],"plug-and-play":[60],"modules":[61],"to":[62,108,187],"enhance":[63],"algorithmic":[64],"robustness":[65],"in":[66,90,197],"along":[70],"with":[71,147],"the":[72,85,91,95,114,127,188,204,215],"self-training":[73],"[1]":[74],"strategy.":[75],"The":[76],"method":[79],"PDD":[80],"randomly":[81],"samples":[82],"perturbs":[84],"ground":[86],"truth":[87],"database":[89],"cloud.":[93],"Meanwhile,":[94],"PDV":[96],"combines":[98],"voxel":[99],"features":[100,105,118],"density-based":[102],"positional":[103],"encoding":[104],"inputs":[107],"multi-head":[110],"self-attention":[111],"module,":[112],"facilitating":[113],"capture":[115],"global":[117],"comprising":[119],"features.":[121],"In":[122],"paper,":[124],"validate":[126],"effectiveness":[128],"typical":[133],"voxel-based":[134],"baseline,":[137],"SecondIOU":[138],"[2],":[139],"conduct":[141],"experiments":[142],"STF":[144],"[3]":[145],"datasets.":[151],"Our":[152,212],"approach":[153],"achieves":[155],"enhancement":[158],"around":[160],"14.9%":[161],"~":[162,175],"25.5%":[163],"<tex":[165,178],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[166,179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$AP_{3D}$</tex>":[167],"(Average":[168,181],"Precision":[169,182],"Of":[170,183],"Boundingboxes)":[172],"17.5%":[174],"29.3%":[176],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$AP_{AOS}$</tex>":[180],"Orientation":[184],"Similarity)":[185],"compared":[186],"baseline":[189,205],"detector":[190],"clear-to-snowy":[192,199],"conditions.":[193,211],"Furthermore,":[194],"PDAST's":[195],"matches":[201],"or":[202],"exceeds":[203],"model":[206],"fully":[207],"supervised":[208],"alleviates":[214],"degradation":[217],"caused":[218],"by":[219],"days,":[225],"is":[227],"great":[229],"significance":[230],"all-weather":[232],"perception.":[233]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}