{"id":"https://openalex.org/W4399938925","doi":"https://doi.org/10.1109/tase.2024.3410891","title":"DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes","display_name":"DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4399938925","doi":"https://doi.org/10.1109/tase.2024.3410891"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2024.3410891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2024.3410891","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059845739","display_name":"Zhuopeng Li","orcid":"https://orcid.org/0009-0002-2110-9040"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhuopeng Li","raw_affiliation_strings":["College of Software, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Software, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103089859","display_name":"Chenming Wu","orcid":"https://orcid.org/0000-0001-8012-1547"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenming Wu","raw_affiliation_strings":["Robotics and Autonomous Driving Laboratory (RAL), Baidu Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Robotics and Autonomous Driving Laboratory (RAL), Baidu Research, Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101843626","display_name":"Liangjun Zhang","orcid":"https://orcid.org/0000-0001-5737-2540"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangjun Zhang","raw_affiliation_strings":["Robotics and Autonomous Driving Laboratory (RAL), Baidu Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Robotics and Autonomous Driving Laboratory (RAL), Baidu Research, Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062252650","display_name":"Jianke Zhu","orcid":"https://orcid.org/0000-0003-1831-0106"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianke Zhu","raw_affiliation_strings":["College of Computer Science, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059845739"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":3.1151,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91622668,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"22","issue":null,"first_page":"4394","last_page":"4407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9970999956130981,"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":0.9970999956130981,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6209725141525269},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5862959623336792},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5793592929840088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5471415519714355},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.4937232434749603},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48515185713768005}],"concepts":[{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6209725141525269},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5862959623336792},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5793592929840088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5471415519714355},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.4937232434749603},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48515185713768005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2024.3410891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2024.3410891","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2368578961","display_name":null,"funder_award_id":"62376244","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2150066425","https://openalex.org/W2158782408","https://openalex.org/W2200971133","https://openalex.org/W2340897893","https://openalex.org/W2912798476","https://openalex.org/W2950689937","https://openalex.org/W2955189650","https://openalex.org/W2982763192","https://openalex.org/W2999862950","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3174272147","https://openalex.org/W3215048956","https://openalex.org/W3215769467","https://openalex.org/W3216473017","https://openalex.org/W3216476011","https://openalex.org/W4200150166","https://openalex.org/W4210985923","https://openalex.org/W4221143294","https://openalex.org/W4221151978","https://openalex.org/W4221155806","https://openalex.org/W4285108207","https://openalex.org/W4286615883","https://openalex.org/W4289656833","https://openalex.org/W4307411952","https://openalex.org/W4312234520","https://openalex.org/W4312280420","https://openalex.org/W4312706422","https://openalex.org/W4313017495","https://openalex.org/W4313031684","https://openalex.org/W4313887267","https://openalex.org/W4327662982","https://openalex.org/W4378450585","https://openalex.org/W4382468453","https://openalex.org/W4383097455","https://openalex.org/W4383108921","https://openalex.org/W4383109009","https://openalex.org/W4383503807","https://openalex.org/W4385289063","https://openalex.org/W4385318467","https://openalex.org/W4385819940","https://openalex.org/W4386066457","https://openalex.org/W4386072012","https://openalex.org/W4386075959","https://openalex.org/W4386076168","https://openalex.org/W4386076483","https://openalex.org/W4387682331","https://openalex.org/W4387968192","https://openalex.org/W4388145520","https://openalex.org/W4389667683","https://openalex.org/W6745935785","https://openalex.org/W6784492152","https://openalex.org/W6850301280","https://openalex.org/W6855030160"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Despite":[0],"the":[1,23,55,65,72,87,91,117,135,141,156,172,239,287,307,313,330],"recent":[2],"success":[3],"of":[4,67,93,127,174,216,355],"Neural":[5,101,198,275],"Radiance":[6,199],"Field":[7,200],"(NeRF),":[8],"it":[9,206,344],"is":[10,189,207],"still":[11,208],"challenging":[12,209],"to":[13,63,89,131,154,196,210,224,254,268,303,328,345],"render":[14],"large-scale":[15],"driving":[16,167,179,218,340,351],"scenes":[17,36,88,180,352],"with":[18,39],"long":[19],"trajectories,":[20],"particularly":[21],"when":[22],"rendering":[24],"quality":[25],"and":[26,149,181,325,342,353],"efficiency":[27,256],"are":[28,58,107,152],"in":[29,176,249,348],"high":[30],"demand.":[31],"Existing":[32],"methods":[33,221],"for":[34],"such":[35],"usually":[37],"involve":[38],"spatial":[40,225],"warping,":[41,226],"geometric":[42,105,150,227,284,326],"supervision":[43],"from":[44,86,116,134,140,229,306,312],"zero-shot":[45,232],"normal":[46,233],"or":[47,50,61,234,237,252],"depth":[48,235],"estimation,":[49],"scene":[51,240],"division":[52],"strategies,":[53],"where":[54],"synthesized":[56],"views":[57],"often":[59,222],"blurry":[60],"fail":[62,253],"meet":[64,255],"requirement":[66],"efficient":[68,214],"rendering.":[69,122,185,296,357],"To":[70,258],"address":[71],"above":[73],"challenges,":[74,261],"this":[75],"paper":[76],"presents":[77],"a":[78,83,94,128,163,264,270,300,321,337],"novel":[79],"framework":[80],"that":[81],"learns":[82],"density":[84,118,137,143,157,266,288,309,315,331],"space":[85,119,267,289],"guide":[90],"construction":[92],"point-based":[95,271],"renderer,":[96,272],"dubbed":[97],"as":[98],"DGNR":[99,175,335],"(Density-Guided":[100],"Rendering).":[102],"In":[103],"DGNR,":[104,279],"priors":[106,285],"no":[108,281],"longer":[109,282],"needed,":[110],"which":[111],"can":[112,247,290],"be":[113,346],"intrinsically":[114],"learned":[115,142,265,314],"through":[120,294],"volumetric":[121],"Specifically,":[123,297],"we":[124,169,262,280,298],"make":[125],"use":[126,299],"differentiable":[129],"renderer":[130,302],"synthesize":[132],"images":[133,251,305],"neural":[136,308],"features":[138,310],"obtained":[139],"space.":[144,158,316,332],"A":[145],"density-based":[146,322],"fusion":[147,323],"module":[148,324],"regularization":[151,327],"proposed":[153,320],"optimize":[155,329],"By":[159],"conducting":[160],"experiments":[161],"on":[162,336],"widely":[164],"used":[165],"autonomous":[166,339],"dataset,":[168],"have":[170,318],"validated":[171],"effectiveness":[173],"synthesizing":[177],"photorealistic":[178],"achieving":[182],"real-time":[183,356],"capable":[184,354],"Our":[186],"project":[187],"page":[188],"available":[190],"at":[191],"<uri":[192,360],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[193,361],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/JOP-Lee/DGNR-Rendering</uri>.":[194,362],"Note":[195],"Practitioners\u2014While":[197],"(NeRF)":[201],"has":[202],"been":[203],"gaining":[204],"attraction,":[205],"create":[211,304],"highly":[212],"detailed,":[213],"renderings":[215],"large":[217],"scenes.":[219],"Current":[220],"resort":[223],"guidance":[228],"tools":[230],"like":[231],"estimates,":[236],"dividing":[238],"into":[241],"smaller":[242],"parts.":[243],"Unfortunately,":[244],"these":[245,260],"techniques":[246],"result":[248],"blurred":[250],"needs.":[257],"solve":[259],"introduce":[263],"build":[269],"termed":[273],"Density-Guided":[274],"Rendering":[276],"(DGNR).":[277],"With":[278],"need":[283],"because":[286],"inherently":[291],"learn":[292],"them":[293],"volume":[295],"flexible":[301],"derived":[311],"We":[317,333],"also":[319],"evaluated":[334],"popular":[338],"dataset":[341],"found":[343],"effective":[347],"creating":[349],"realistic":[350],"Project":[358],"page:":[359]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}
