{"id":"https://openalex.org/W7165753089","doi":"https://doi.org/10.48550/arxiv.2606.24301","title":"MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving","display_name":"MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165753089","doi":"https://doi.org/10.48550/arxiv.2606.24301"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24301","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.2606.24301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139230991","display_name":"Hongli Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Hongli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021832931","display_name":"Youjian Zhang","orcid":"https://orcid.org/0000-0002-5255-9258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Youjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001930695","display_name":"Yucai Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Yucai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139295063","display_name":"Chaoyue Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chaoyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112642833","display_name":"Y Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Yaohui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100633468","display_name":"Xiaoguang Ren","orcid":"https://orcid.org/0000-0002-8048-6762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Xiaoguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139250196","display_name":"Wenjing Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wenjing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139290204","display_name":"Long Lan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lan, Long","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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.3361999988555908,"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":0.3361999988555908,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.24050000309944153,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.09200000017881393,"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/point-cloud","display_name":"Point cloud","score":0.7756999731063843},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6298999786376953},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.5968000292778015},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5013999938964844},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.49239999055862427},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.4652999937534332},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.43299999833106995},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.37290000915527344},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.35589998960494995}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7756999731063843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7727000117301941},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6298999786376953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6173999905586243},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6136000156402588},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5968000292778015},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.37290000915527344},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.35589998960494995},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.3188999891281128},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2770000100135803},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2777897806","wikidata":"https://www.wikidata.org/wiki/Q568742","display_name":"3D modeling","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.25850000977516174},{"id":"https://openalex.org/C193581530","wikidata":"https://www.wikidata.org/wiki/Q683778","display_name":"Structured light","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C68537008","wikidata":"https://www.wikidata.org/wiki/Q247932","display_name":"Stereopsis","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24301","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.2606.24301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24301","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":[{"score":0.4764274060726166,"id":"https://metadata.un.org/sdg/9","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":{"Recovering":[0],"realistic":[1],"3D":[2,51,87,102,164,187],"vehicle":[3,22,88,188],"models":[4,53],"from":[5,96],"autonomous":[6,97],"driving":[7,72,98],"scenes":[8],"is":[9,191],"crucial":[10],"for":[11,63,85],"synthesizing":[12],"training":[13],"data":[14],"and":[15,34,38,67,93,124,147,170],"building":[16],"simulation":[17],"environment.":[18],"However,":[19],"most":[20],"existing":[21,183],"generation":[23,89],"methods":[24,184],"fail":[25],"to":[26,46,120,167],"fully":[27],"exploit":[28],"multimodal":[29],"sensors":[30,95],"i.e.":[31],"multi-view":[32,65,106],"images":[33,107],"LiDAR":[35,92,114],"point":[36,115,134,150],"clouds)":[37],"rely":[39],"on":[40,160,176],"neural":[41],"rendering":[42],"based":[43,159],"reconstruction,":[44],"leading":[45],"low-quality":[47],"mesh.":[48],"Recently,":[49],"native":[50,101],"generative":[52,103],"have":[54],"made":[55],"significant":[56],"progress,":[57],"yet":[58],"they":[59],"are":[60,108],"not":[61],"built":[62],"arbitrary":[64],"inputs":[66],"often":[68],"struggle":[69],"with":[70,136],"in-the-wild":[71,86],"images.":[73],"In":[74],"this":[75],"work,":[76],"we":[77,129,153],"present":[78],"MM-TRELLIS,":[79],"a":[80,155],"multi-modal":[81],"version":[82],"of":[83,163],"TRELLIS":[84],"that":[90],"integrates":[91],"image":[94],"datasets":[99],"into":[100],"models.":[104],"Specifically,":[105],"cycled":[109],"as":[110],"conditioning":[111],"inputs,":[112],"while":[113],"clouds":[116],"provide":[117],"test-time":[118],"guidance":[119,133,149],"ensure":[121],"geometric":[122],"accuracy":[123],"cross-view":[125],"consistency.":[126],"During":[127],"denoising,":[128],"first":[130],"align":[131],"the":[132,137,144,148,161],"cloud":[135],"model":[138],"priors,":[139],"then":[140],"enforce":[141],"consistency":[142],"between":[143],"generated":[145],"geometry":[146],"cloud.":[151],"Finally,":[152],"introduce":[154],"voxel":[156],"filtering":[157],"strategy":[158],"opacity":[162],"Gaussian":[165],"Splatting":[166],"suppress":[168],"floaters":[169],"produce":[171],"clean":[172],"meshes.":[173],"Comprehensive":[174],"experiments":[175],"Waymo":[177],"dataset":[178],"demonstrate":[179],"our":[180],"method":[181],"outperforms":[182],"in":[185],"high-fidelity":[186],"generation.":[189],"Code":[190],"available":[192],"at":[193],"https://github.com/HongliXiao/MM-TRELLIS.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
