{"id":"https://openalex.org/W7169768897","doi":"https://doi.org/10.48550/arxiv.2607.16015","title":"PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects","display_name":"PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects","publication_year":2026,"publication_date":"2026-07-17","ids":{"openalex":"https://openalex.org/W7169768897","doi":"https://doi.org/10.48550/arxiv.2607.16015"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.16015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16015","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.16015","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141222224","display_name":"Leon Jungemeyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jungemeyer, Leon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044022807","display_name":"Alejandro Maga\u00f1a","orcid":"https://orcid.org/0000-0002-2302-5695"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maga\u00f1a, Alejandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141191310","display_name":"Gautham Mohan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohan, Gautham","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086190105","display_name":"Matthias Karl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karl, Matthias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036666927","display_name":"Daniel Werdehausen","orcid":"https://orcid.org/0000-0002-1559-0811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Werdehausen, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9147999882698059,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9147999882698059,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.024000000208616257,"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"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.016300000250339508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.7828999757766724},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.6657000184059143},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5508999824523926},{"id":"https://openalex.org/keywords/articulated-body-pose-estimation","display_name":"Articulated body pose estimation","score":0.5450000166893005},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4544999897480011},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43630000948905945},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.43560001254081726},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.400299996137619}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8729000091552734},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.8112000226974487},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7828999757766724},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.6657000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291999816894531},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5508999824523926},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.5450000166893005},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.400299996137619},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.38530001044273376},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.357699990272522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29010000824928284},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.26109999418258667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.16015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16015","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.16015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16015","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.4843392074108124}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"6D":[0,55],"pose":[1,56,103],"estimation":[2],"remains":[3],"a":[4,49,88,148],"key":[5],"challenge":[6],"in":[7,13],"robotics":[8],"and":[9,35,72,81,116,128,146,156],"computer":[10],"vision,":[11],"particularly":[12],"industrial":[14],"environments.":[15],"The":[16],"deployment":[17],"of":[18,57],"currently":[19],"available":[20],"data-driven":[21],"methods":[22],"is":[23],"often":[24],"limited":[25],"by":[26,41],"resource-intensive":[27],"data":[28],"pipelines,":[29],"reliance":[30],"on":[31,107,133,140],"textured":[32],"3D":[33,68],"models,":[34],"sensitivity":[36],"to":[37,83,97,114,158],"geometric":[38,123],"deviations":[39,124],"caused":[40],"damages":[42],"or":[43],"assembly":[44,152],"defects.":[45],"We":[46,131],"present":[47],"PIXIE,":[48],"zero-shot":[50],"framework":[51],"that":[52],"estimates":[53],"the":[54,84,110,126],"an":[58,61,66],"object":[59],"from":[60,77],"RGB":[62],"image":[63,86],"using":[64],"only":[65],"untextured":[67],"model.":[69],"Synthetic":[70],"depth":[71],"normal":[73],"maps":[74],"are":[75,95],"rendered":[76],"sampled":[78],"reference":[79],"viewpoints":[80],"matched":[82],"query":[85],"via":[87],"pretrained":[89],"cross-modality":[90],"feature":[91],"matcher.":[92],"Matched":[93],"keypoints":[94],"back-projected":[96],"obtain":[98],"2D--3D":[99],"correspondences":[100],"for":[101],"PnP-based":[102],"estimation.":[104],"Relying":[105],"exclusively":[106],"geometry":[108],"makes":[109],"method":[111],"inherently":[112],"robust":[113],"lighting":[115],"texture":[117,154],"variation,":[118],"while":[119],"correspondence":[120],"filtering":[121],"handles":[122],"between":[125],"model":[127],"physical":[129],"object.":[130],"evaluate":[132],"widely-used":[134],"public":[135],"benchmarks,":[136],"reporting":[137],"state-of-the-art":[138],"results":[139],"texture-less":[141],"objects":[142],"without":[143],"object-specific":[144],"training,":[145],"introduce":[147],"novel":[149],"dataset":[150],"with":[151],"defects,":[153],"variations,":[155],"occlusion":[157],"demonstrate":[159],"real-world":[160],"applicability.":[161]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-21T00:00:00"}
