{"id":"https://openalex.org/W7163134950","doi":"https://doi.org/10.48550/arxiv.2606.01590","title":"Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis","display_name":"Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163134950","doi":"https://doi.org/10.48550/arxiv.2606.01590"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01590","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.2606.01590","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137633108","display_name":"Zhengfei Kuang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuang, Zhengfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137696835","display_name":"Adam Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Adam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100844750","display_name":"Liyuan Zhu","orcid":"https://orcid.org/0000-0003-1702-1118"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Liyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137671304","display_name":"Tong Wu","orcid":"https://orcid.org/0009-0005-7868-3336"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008937103","display_name":"Shengqu Cai","orcid":"https://orcid.org/0000-0002-3063-9644"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Shengqu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137681442","display_name":"Jonathan Tremblay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tremblay, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014426007","display_name":"Iro Armeni","orcid":"https://orcid.org/0000-0002-4230-5916"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Armeni, Iro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137629832","display_name":"Ehsan Adeli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adeli, Ehsan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070302579","display_name":"Lior Yariv","orcid":"https://orcid.org/0009-0002-2164-9223"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yariv, Lior","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137641524","display_name":"Gordon Wetzstein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wetzstein, Gordon","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/T10531","display_name":"Advanced Vision and Imaging","score":0.6263999938964844,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.6263999938964844,"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":0.09910000115633011,"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":0.06069999933242798,"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/exploit","display_name":"Exploit","score":0.6629999876022339},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6240000128746033},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6107000112533569},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5432000160217285},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5085999965667725},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.45680001378059387},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.45570001006126404},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4296000003814697}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7724000215530396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6635000109672546},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6629999876022339},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6334999799728394},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6240000128746033},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6107000112533569},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5432000160217285},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5085999965667725},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.45680001378059387},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.45570001006126404},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4296000003814697},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C2777634741","wikidata":"https://www.wikidata.org/wiki/Q768993","display_name":"Wasserstein metric","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C182124507","wikidata":"https://www.wikidata.org/wiki/Q166154","display_name":"Line segment","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01590","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.2606.01590","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01590","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":[{"id":"https://metadata.un.org/sdg/11","score":0.7598675489425659,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"vehicle":[1,56],"platforms":[2],"are":[3],"equipped":[4],"with":[5],"a":[6,26,65,92,127,139,146,153],"rich":[7],"sensor":[8],"suite,":[9],"including":[10],"LiDAR,":[11],"calibrated":[12],"multi-camera":[13],"rigs,":[14],"and":[15,70,115,208],"accurate":[16,100],"ego-motion,":[17],"that":[18,88,131,158,183],"in":[19],"principle":[20],"offers":[21],"strong":[22],"signal":[23],"for":[24,42],"re-rendering":[25],"driving":[27,84],"scene":[28],"from":[29,54,81],"novel":[30,52],"viewpoints.":[31],"A":[32],"growing":[33],"line":[34],"of":[35,67,195],"recent":[36],"work":[37],"leverages":[38],"video":[39,128],"diffusion":[40,129],"models":[41],"this":[43,68,89],"task,":[44],"using":[45],"their":[46,71],"generative":[47],"priors":[48],"to":[49,74,163],"synthesize":[50],"plausible":[51],"views":[53],"sparse":[55,96,165,178],"observations.":[57],"In":[58],"practice,":[59],"however,":[60],"existing":[61],"methods":[62,182],"exploit":[63],"only":[64],"fragment":[66],"signal,":[69],"quality":[72],"tends":[73],"degrade":[75],"as":[76,204],"the":[77,82,119,161,168],"target":[78],"trajectory":[79],"departs":[80],"recorded":[83],"path.":[85],"We":[86,124,151,191],"argue":[87],"is":[90],"fundamentally":[91],"multi-sensor":[93],"fusion":[94],"problem:":[95],"LiDAR":[97,179],"reprojections":[98],"supply":[99],"but":[101,111],"incomplete":[102],"metric":[103,113],"geometry,":[104],"surround-view":[105],"reference":[106],"imagery":[107],"supplies":[108],"dense":[109],"appearance":[110],"no":[112],"depth,":[114],"camera":[116],"poses":[117],"tie":[118],"two":[120],"together":[121],"across":[122],"views.":[123],"introduce":[125],"StreetNVS,":[126],"framework":[130],"jointly":[132],"conditions":[133],"on":[134,145,185],"all":[135],"three":[136],"signals":[137],"through":[138],"Reference-Enhanced":[140],"Camera":[141],"Attention":[142],"module":[143],"based":[144],"relative":[147],"ray-level":[148],"positional":[149],"encoding.":[150],"develop":[152],"two-stage":[154],"curriculum":[155],"training":[156],"strategy":[157],"gradually":[159],"exposes":[160],"model":[162],"increasingly":[164],"LiDAR.":[166],"On":[167],"Waymo":[169],"Open":[170],"Dataset,":[171],"StreetNVS":[172],"substantially":[173],"outperforms":[174],"state-of-the-art":[175],"baselines":[176],"under":[177],"conditioning,":[180],"matches":[181],"rely":[184],"10-100":[186],"times":[187],"denser":[188],"point":[189],"clouds.":[190],"further":[192],"show":[193],"capabilities":[194],"synthesizing":[196],"coherent":[197],"videos":[198],"along":[199],"extreme":[200],"out-of-trajectory":[201],"paths":[202],"such":[203],"elevation,":[205],"lane-shift,":[206],"pullback,":[207],"rotation.":[209],"Our":[210],"website:":[211],"https://streetnvs.github.io":[212]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}