{"id":"https://openalex.org/W7151274572","doi":"https://doi.org/10.48550/arxiv.2604.03972","title":"Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection","display_name":"Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection","publication_year":2026,"publication_date":"2026-04-05","ids":{"openalex":"https://openalex.org/W7151274572","doi":"https://doi.org/10.48550/arxiv.2604.03972"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03972","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03972","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.03972","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089529572","display_name":"Xueyang Kang","orcid":"https://orcid.org/0000-0001-7159-676X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kang, Xueyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133074151","display_name":"Zizhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zizhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133135311","display_name":"Tian Lan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lan, Tian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101768095","display_name":"Dong Gong","orcid":"https://orcid.org/0000-0002-2668-9630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Dong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133104570","display_name":"Kourosh Khoshelham","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khoshelham, Kourosh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027616562","display_name":"Liangliang Nan","orcid":"https://orcid.org/0000-0002-5629-9975"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nan, Liangliang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5089529572"],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.287200003862381,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.287200003862381,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.20589999854564667,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.08969999849796295,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6980999708175659},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6274999976158142},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6047000288963318},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.43950000405311584},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.424699991941452},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4104999899864197},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.38670000433921814},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.38510000705718994}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6980999708175659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6875},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6274999976158142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6168000102043152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6047000288963318},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.43950000405311584},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3824999928474426},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2596000134944916},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2581999897956848},{"id":"https://openalex.org/C56435381","wikidata":"https://www.wikidata.org/wiki/Q1196371","display_name":"Geometric transformation","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2522999942302704},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03972","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03972","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03972","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03972","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6083303689956665,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"3D":[0],"shape":[1],"anomaly":[2,41,75,90,149],"detection":[3,30],"is":[4],"a":[5,72],"crucial":[6],"task":[7],"for":[8,88],"industrial":[9,115,132,148],"inspection":[10],"and":[11,24,43,55,84,110,125,131,137,151,159,166],"geometric":[12,48],"analysis.":[13],"Existing":[14],"deep":[15],"learning":[16],"approaches":[17],"typically":[18],"learn":[19],"representations":[20],"of":[21,155],"normal":[22],"shapes":[23],"identify":[25],"anomalies":[26],"via":[27],"out-of-distribution":[28],"feature":[29],"or":[31,60],"decoder-based":[32],"reconstruction.":[33],"They":[34],"often":[35],"fail":[36],"to":[37,58,99],"generalize":[38],"across":[39],"diverse":[40],"types":[42],"scales,":[44],"such":[45],"as":[46],"global":[47],"errors":[49],"(e.g.,":[50],"planar":[51],"shifts,":[52],"angle":[53],"misalignments),":[54],"are":[56],"sensitive":[57],"noisy":[59],"incomplete":[61],"local":[62,85],"points":[63],"during":[64],"training.":[65],"To":[66],"address":[67],"these":[68],"limitations,":[69],"we":[70,112],"propose":[71],"hierarchical":[73],"point-patch":[74],"scoring":[76],"network":[77],"that":[78],"jointly":[79],"models":[80,121],"regional":[81],"part":[82],"features":[83,87],"point":[86],"robust":[89],"reasoning.":[91],"An":[92],"adaptive":[93],"patchification":[94],"module":[95],"integrates":[96],"self-supervised":[97],"decomposition":[98],"capture":[100],"complex":[101],"structural":[102,126],"deviations.":[103],"Beyond":[104],"evaluations":[105],"on":[106,129,145,157,161],"public":[107,130],"benchmarks":[108],"(Anomaly-ShapeNet":[109],"Real3D-AD),":[111],"release":[113],"an":[114],"test":[116],"set":[117],"with":[118],"real":[119],"CAD":[120],"exhibiting":[122],"planar,":[123],"angular,":[124],"defects.":[127],"Experiments":[128],"datasets":[133],"show":[134],"superior":[135],"AUC-ROC":[136],"AUC-PR":[138],"performance,":[139],"including":[140],"over":[141],"40%":[142],"point-level":[143],"improvement":[144],"the":[146],"new":[147],"type":[150],"average":[152],"object-level":[153],"gains":[154],"7%":[156],"Real3D-AD":[158],"4%":[160],"Anomaly-ShapeNet,":[162],"demonstrating":[163],"strong":[164],"robustness":[165],"generalization.":[167]},"counts_by_year":[],"updated_date":"2026-04-08T06:07:18.267832","created_date":"2026-04-08T00:00:00"}