{"id":"https://openalex.org/W4387968639","doi":"https://doi.org/10.1145/3581783.3612320","title":"Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation","display_name":"Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387968639","doi":"https://doi.org/10.1145/3581783.3612320"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612320","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5006900997","display_name":"Yiyang Chen","orcid":"https://orcid.org/0000-0001-5976-7888"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiyang Chen","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5976-7888","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101873694","display_name":"Shanshan Zhao","orcid":"https://orcid.org/0000-0003-0682-8645"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanshan Zhao","raw_affiliation_strings":["JD Explore Academy, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0682-8645","affiliations":[{"raw_affiliation_string":"JD Explore Academy, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038748720","display_name":"Changxing Ding","orcid":"https://orcid.org/0000-0001-7232-3181"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changxing Ding","raw_affiliation_strings":["South China University of Technology & Pazhou Lab, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7232-3181","affiliations":[{"raw_affiliation_string":"South China University of Technology & Pazhou Lab, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038237902","display_name":"Liyao Tang","orcid":"https://orcid.org/0009-0004-9443-4068"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Liyao Tang","raw_affiliation_strings":["The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0009-0004-9443-4068","affiliations":[{"raw_affiliation_string":"The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101701657","display_name":"Chaoyue Wang","orcid":"https://orcid.org/0000-0002-9002-1029"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyue Wang","raw_affiliation_strings":["JD Explore Academy, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9002-1029","affiliations":[{"raw_affiliation_string":"JD Explore Academy, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074103823","display_name":"Dacheng Tao","orcid":"https://orcid.org/0000-0001-7225-5449"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dacheng Tao","raw_affiliation_strings":["The University of Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7225-5449","affiliations":[{"raw_affiliation_string":"The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5006900997"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.5063,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72099579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3866","last_page":"3875"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9983000159263611,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9965000152587891,"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/lidar","display_name":"Lidar","score":0.8348398208618164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7997268438339233},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6654912233352661},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6039119958877563},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5639903545379639},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5518098473548889},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5170962810516357},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5008144378662109},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4556446969509125},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4438386559486389},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43421387672424316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3821943700313568},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.282210111618042},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07763054966926575},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07685476541519165}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8348398208618164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7997268438339233},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6654912233352661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6039119958877563},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5639903545379639},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5518098473548889},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5170962810516357},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5008144378662109},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4556446969509125},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4438386559486389},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43421387672424316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3821943700313568},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.282210111618042},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07763054966926575},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07685476541519165},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612320","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1590409721","display_name":null,"funder_award_id":"2023A1515010007","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G2474074552","display_name":null,"funder_award_id":"62076101","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"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2131396337","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2487365028","https://openalex.org/W2556802233","https://openalex.org/W2560609797","https://openalex.org/W2612690371","https://openalex.org/W2788158258","https://openalex.org/W2962912109","https://openalex.org/W2963073217","https://openalex.org/W2963182550","https://openalex.org/W2968557240","https://openalex.org/W2979750740","https://openalex.org/W2992308087","https://openalex.org/W3035574168","https://openalex.org/W3035750252","https://openalex.org/W3109944402","https://openalex.org/W3120804725","https://openalex.org/W3129559111","https://openalex.org/W3172507977","https://openalex.org/W3177015573","https://openalex.org/W3180638445","https://openalex.org/W3201726317","https://openalex.org/W3202115483","https://openalex.org/W4206398307","https://openalex.org/W4225649887","https://openalex.org/W4296580612","https://openalex.org/W4304092477","https://openalex.org/W4312251571","https://openalex.org/W4312699854","https://openalex.org/W4312708188","https://openalex.org/W4312894203","https://openalex.org/W4386065958"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W4281783339","https://openalex.org/W1964041166","https://openalex.org/W4293094720","https://openalex.org/W2739701376"],"abstract_inverted_index":{"In":[0,174],"recent":[1],"years,":[2],"cross-modal":[3,162],"domain":[4,47],"adaptation":[5],"has":[6],"been":[7],"studied":[8],"on":[9,87,208],"the":[10,20,30,38,45,55,60,68,78,88,148,160,165,191,214,217,224],"paired":[11,39,112],"2D":[12,40,69,90,104,115,151,167],"image":[13,116,152,168],"and":[14,41,84,110,117,133,153,158,169,185,196],"3D":[15,24,42,61,82,118,155,170],"LiDAR":[16,25,119,156,171,228],"data":[17,43,83,120,172],"to":[18,63,178,189,234],"ease":[19],"labeling":[21],"costs":[22],"for":[23],"semantic":[26,65,108],"segmentation":[27,222],"(3DLSS)":[28],"in":[29,34,44,127,193,202,213],"target":[31,154,166],"domain.":[32],"However,":[33],"such":[35,182],"a":[36,98,103,111],"setting":[37,101],"source":[46,81,89,150],"are":[48,122,231],"still":[49],"collected":[50],"with":[51,107],"additional":[52],"effort.":[53],"Since":[54],"2D-3D":[56],"projections":[57],"can":[58,220],"enable":[59],"model":[62],"learn":[64],"information":[66],"from":[67],"counterpart,":[70],"we":[71,74,130,176],"ask":[72],"whether":[73],"could":[75],"further":[76],"remove":[77],"need":[79],"of":[80,226],"only":[85],"rely":[86],"images.":[91],"To":[92,124],"answer":[93],"it,":[94],"this":[95,128],"paper":[96],"studies":[97],"new":[99],"3DLSS":[100,126],"where":[102],"dataset":[105],"(source)":[106],"annotations":[109],"but":[113],"unannotated":[114],"(target)":[121],"available1.":[123],"achieve":[125,221],"scenario,":[129],"propose":[131,177],"Cross-Modal":[132],"Cross-Domain":[134],"Learning":[135],"(CoMoDaL).":[136],"Specifically,":[137],"our":[138],"CoMoDaL":[139,219],"aims":[140],"at":[141],"modeling":[142],"1)":[143],"inter-modal":[144],"cross-domain":[145],"distillation":[146],"between":[147,164],"unpaired":[149],"data,":[157],"2)":[159],"intra-domain":[161],"guidance":[163],"pair.":[173],"CoMoDaL,":[175],"apply":[179],"several":[180,209],"constraints,":[181],"as":[183],"point-to-pixel":[184],"prototype-to":[186],"pixel":[187],"alignments,":[188],"associate":[190],"semantics":[192],"different":[194],"modalities":[195],"domains":[197],"by":[198],"constructing":[199],"mixed":[200],"samples":[201],"two":[203],"modalities.":[204],"The":[205],"experimental":[206],"results":[207],"datasets":[210],"show":[211],"that":[212],"proposed":[215],"setting,":[216],"developed":[218],"without":[223],"supervision":[225],"labeled":[227],"data.":[229],"Ablations":[230],"also":[232],"conducted":[233],"provide":[235],"more":[236],"analysis.":[237],"Code":[238],"will":[239],"be":[240],"available":[241],"publicly2.":[242]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}