{"id":"https://openalex.org/W7155096405","doi":"https://doi.org/10.48550/arxiv.2604.18468","title":"Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation","display_name":"Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155096405","doi":"https://doi.org/10.48550/arxiv.2604.18468"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18468","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18468","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":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.2604.18468","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043812968","display_name":"Tianshi Cao","orcid":"https://orcid.org/0000-0001-6579-6044"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Tianshi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134165405","display_name":"Jiawei Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134132422","display_name":"Yuxuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599618","display_name":"Jaewoo Seo","orcid":"https://orcid.org/0000-0002-8949-0020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, Jaewoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134115555","display_name":"Jiahui Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134107864","display_name":"Shikhar Solanki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Solanki, Shikhar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134206056","display_name":"Haotian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haotian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134207840","display_name":"Mingfei Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Mingfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134134620","display_name":"Haithem Turki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Turki, Haithem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011573512","display_name":"Muxingzi Li","orcid":"https://orcid.org/0000-0003-2126-8578"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Muxingzi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134104925","display_name":"Yue Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129731068","display_name":"Sipeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Sipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134114371","display_name":"Zan Gojcic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gojcic, Zan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070642269","display_name":"Sanja Fidler","orcid":"https://orcid.org/0000-0003-1040-3260"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fidler, Sanja","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026664609","display_name":"Kangxue Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Kangxue","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.359499990940094,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.359499990940094,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.1590999960899353,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.09099999815225601,"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/pipeline","display_name":"Pipeline (software)","score":0.6798999905586243},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6588000059127808},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5942000150680542},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.511900007724762},{"id":"https://openalex.org/keywords/asset","display_name":"Asset (computer security)","score":0.5041999816894531},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4950999915599823},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43849998712539673},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3725999891757965},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3546000123023987}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6948999762535095},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6798999905586243},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6588000059127808},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5942000150680542},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.511900007724762},{"id":"https://openalex.org/C76178495","wikidata":"https://www.wikidata.org/wiki/Q4808784","display_name":"Asset (computer security)","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4950999915599823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4494999945163727},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4065000116825104},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.25540000200271606},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18468","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18468","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":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.2604.18468","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18468","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Closed-loop":[0],"simulation":[1],"is":[2,126],"a":[3,82,88,109],"core":[4],"component":[5],"of":[6,99,151],"autonomous":[7],"vehicle":[8],"(AV)":[9],"development,":[10],"enabling":[11],"scalable":[12,149],"testing,":[13],"training,":[14],"and":[15,46,61,108,133,144],"safety":[16],"validation":[17],"before":[18],"real-world":[19,92,135],"deployment.":[20],"Neural":[21],"scene":[22],"reconstruction":[23],"converts":[24,65],"driving":[25,72],"logs":[26,73],"into":[27,74,156],"interactive":[28],"3D":[29,39,119,158],"environments":[30],"for":[31,43,91],"simulation,":[32],"but":[33],"it":[34],"does":[35],"not":[36],"produce":[37],"complete":[38],"object":[40,68,154],"assets":[41],"required":[42],"agent":[44],"manipulation":[45],"large-viewpoint":[47],"novel-view":[48],"synthesis.":[49],"To":[50],"address":[51,130],"this":[52,123,146],"challenge,":[53],"we":[54,86],"present":[55],"Asset":[56],"Harvester,":[57],"an":[58],"image-to-3D":[59],"model":[60,84],"end-to-end":[62],"pipeline":[63],"that":[64,95,113],"sparse,":[66],"in-the-wild":[67],"observations":[69,155],"from":[70],"real":[71],"complete,":[75],"simulation-ready":[76],"assets.":[77,159],"Rather":[78],"than":[79],"relying":[80],"on":[81],"single":[83],"component,":[85],"developed":[87],"system-level":[89],"design":[90],"AV":[93,153],"data":[94,136,141],"combines":[96],"large-scale":[97],"curation":[98],"object-centric":[100],"training":[101,111],"tuples,":[102],"geometry-aware":[103],"preprocessing":[104],"across":[105],"heterogeneous":[106],"sensors,":[107],"robust":[110],"recipe":[112],"couples":[114],"sparse-view-conditioned":[115],"multiview":[116],"generation":[117],"with":[118,139],"Gaussian":[120],"lifting.":[121],"Within":[122],"system,":[124],"SparseViewDiT":[125],"explicitly":[127],"designed":[128],"to":[129],"limited-angle":[131],"views":[132],"other":[134],"challenges.":[137],"Together":[138],"hybrid":[140],"curation,":[142],"augmentation,":[143],"self-distillation,":[145],"system":[147],"enables":[148],"conversion":[150],"sparse":[152],"reusable":[157]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-22T00:00:00"}
