{"id":"https://openalex.org/W4389666296","doi":"https://doi.org/10.1109/iros55552.2023.10342338","title":"Enhance Local Feature Consistency with Structure Similarity Loss for 3D Semantic Segmentation","display_name":"Enhance Local Feature Consistency with Structure Similarity Loss for 3D Semantic Segmentation","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4389666296","doi":"https://doi.org/10.1109/iros55552.2023.10342338"},"language":"en","primary_location":{"id":"doi:10.1109/iros55552.2023.10342338","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iros55552.2023.10342338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5111092067","display_name":"Cheng- Wei Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng- Wei Lin","raw_affiliation_strings":["National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093479898","display_name":"Fang-Yu Syu","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Fang-Yu Syu","raw_affiliation_strings":["National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068479690","display_name":"Yi\u2010Ju Pan","orcid":"https://orcid.org/0000-0002-4265-0736"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Ju Pan","raw_affiliation_strings":["National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101426057","display_name":"Kuan\u2010Wen Chen","orcid":"https://orcid.org/0000-0002-4159-201X"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuan-Wen Chen","raw_affiliation_strings":["National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan,300","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36605574,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"55","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9987999796867371,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9987999796867371,"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/overfitting","display_name":"Overfitting","score":0.7779399156570435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7745255827903748},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6936178803443909},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6745577454566956},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6661829948425293},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6319210529327393},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6138441562652588},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.580028235912323},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.560850977897644},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4778839647769928},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4633636176586151},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.45559361577033997},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4114794135093689},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39190948009490967},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.095754474401474},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08516323566436768},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07614636421203613}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7779399156570435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7745255827903748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6936178803443909},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6745577454566956},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6661829948425293},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6319210529327393},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6138441562652588},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.580028235912323},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.560850977897644},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4778839647769928},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4633636176586151},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.45559361577033997},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4114794135093689},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39190948009490967},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.095754474401474},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08516323566436768},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07614636421203613},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros55552.2023.10342338","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iros55552.2023.10342338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1920022804","https://openalex.org/W2008680434","https://openalex.org/W2460657278","https://openalex.org/W2606202972","https://openalex.org/W2614059183","https://openalex.org/W2795014656","https://openalex.org/W2797997528","https://openalex.org/W2897855555","https://openalex.org/W2955873422","https://openalex.org/W2960986959","https://openalex.org/W2963125977","https://openalex.org/W2963158438","https://openalex.org/W2963182550","https://openalex.org/W2963231572","https://openalex.org/W2963281829","https://openalex.org/W2963312728","https://openalex.org/W2963640720","https://openalex.org/W2963706542","https://openalex.org/W2981983525","https://openalex.org/W2990613095","https://openalex.org/W2997951504","https://openalex.org/W3007809903","https://openalex.org/W3010797203","https://openalex.org/W3012494314","https://openalex.org/W3034482224","https://openalex.org/W3039448353","https://openalex.org/W3092946149","https://openalex.org/W3107479685","https://openalex.org/W3107518100","https://openalex.org/W3110503160","https://openalex.org/W3119816767","https://openalex.org/W3167855660","https://openalex.org/W3168037706","https://openalex.org/W3171215128","https://openalex.org/W3171433839","https://openalex.org/W3174926460","https://openalex.org/W3177251636","https://openalex.org/W3207531210","https://openalex.org/W3208106571","https://openalex.org/W6763422710","https://openalex.org/W6802749810"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"Recently,":[0],"many":[1],"research":[2],"studies":[3],"have":[4],"been":[5],"carried":[6],"out":[7],"on":[8,25,88,135],"using":[9],"deep":[10,55,81],"learning":[11,56,79],"methods":[12],"for":[13,91],"3D":[14,26,42],"point":[15,27,92],"cloud":[16,28],"understanding.":[17],"However,":[18],"there":[19],"is":[20,40,58,72],"still":[21],"no":[22],"remarkable":[23],"result":[24],"semantic":[29,89],"segmentation":[30,90],"compared":[31],"to":[32,60,64,73,77,101,114],"those":[33],"of":[34,80],"2D":[35],"research.":[36],"One":[37],"important":[38],"reason":[39],"that":[41,53,128],"data":[43],"has":[44],"higher":[45],"dimensionality":[46],"but":[47],"lacks":[48],"large":[49],"datasets,":[50],"which":[51],"means":[52],"the":[54,78,95,102,120,129],"model":[57],"difficult":[59],"optimize":[61],"and":[62,112,138,143],"easy":[63],"overfit.":[65],"To":[66,98],"overcome":[67],"this,":[68],"an":[69],"essential":[70],"method":[71,131],"provide":[74,99],"more":[75],"priors":[76,100],"models.":[82],"In":[83],"this":[84],"paper,":[85],"we":[86,104],"focus":[87],"clouds":[93],"in":[94,119],"real":[96],"world.":[97],"model,":[103],"propose":[105],"a":[106],"novel":[107],"loss":[108],"function":[109],"called":[110],"Linearity":[111],"Planarity":[113],"enhance":[115],"local":[116,124],"feature":[117],"consistency":[118],"regions":[121],"with":[122],"similar":[123],"structure.":[125],"Experiments":[126],"show":[127],"proposed":[130],"improves":[132],"baseline":[133],"performance":[134],"both":[136],"indoor":[137],"outdoor":[139],"datasets":[140],"e.g.":[141],"S3DIS":[142],"Semantic3D.":[144]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}