{"id":"https://openalex.org/W7160334098","doi":"https://doi.org/10.1109/wacv61042.2026.00053","title":"MDUNet: Multimodal Decoding UNet for Passive Occluder-Aided Non-line-of-sight 3D Imaging","display_name":"MDUNet: Multimodal Decoding UNet for Passive Occluder-Aided Non-line-of-sight 3D Imaging","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160334098","doi":"https://doi.org/10.1109/wacv61042.2026.00053"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-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/A5020020666","display_name":"Fadlullah Raji","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fadlullah Raji","raw_affiliation_strings":["University of South Florida,Bellini College of AI, Cybersecurity and Computing,Tampa,FL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida,Bellini College of AI, Cybersecurity and Computing,Tampa,FL","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134143624","display_name":"John Murray-Bruce","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Murray-Bruce","raw_affiliation_strings":["University of South Florida,Bellini College of AI, Cybersecurity and Computing,Tampa,FL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Florida,Bellini College of AI, Cybersecurity and Computing,Tampa,FL","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60648554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"461","last_page":"471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.6065999865531921,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.6065999865531921,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.03720000013709068,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11569","display_name":"Optical Coherence Tomography Applications","score":0.03700000047683716,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/decoding-methods","display_name":"Decoding methods","score":0.5257999897003174},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.2996000051498413},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.2939999997615814},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.26589998602867126},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.26100000739097595}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6090999841690063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5929999947547913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5425999760627747},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5257999897003174},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1537106439","https://openalex.org/W2112858385","https://openalex.org/W2167707793","https://openalex.org/W2259301194","https://openalex.org/W2538216436","https://openalex.org/W2613985060","https://openalex.org/W2777077087","https://openalex.org/W2793604749","https://openalex.org/W2798602665","https://openalex.org/W2913322182","https://openalex.org/W2949724557","https://openalex.org/W2952225226","https://openalex.org/W2955090919","https://openalex.org/W2959851426","https://openalex.org/W2966437379","https://openalex.org/W2972986791","https://openalex.org/W2995957055","https://openalex.org/W3004617929","https://openalex.org/W3015038906","https://openalex.org/W3015781547","https://openalex.org/W3024020995","https://openalex.org/W3035697917","https://openalex.org/W3103730686","https://openalex.org/W3108047812","https://openalex.org/W3110621803","https://openalex.org/W3140413839","https://openalex.org/W3159198872","https://openalex.org/W3159927738","https://openalex.org/W3173531806","https://openalex.org/W3182048377","https://openalex.org/W3211761719","https://openalex.org/W3215740530","https://openalex.org/W4214730249","https://openalex.org/W4233857083","https://openalex.org/W4285120867","https://openalex.org/W4297097666","https://openalex.org/W4300860943","https://openalex.org/W4319300141","https://openalex.org/W4381597445","https://openalex.org/W4385486456","https://openalex.org/W4386065644","https://openalex.org/W4386066447","https://openalex.org/W4386075660","https://openalex.org/W4386076097","https://openalex.org/W4388416492","https://openalex.org/W4388928718","https://openalex.org/W4390874067","https://openalex.org/W4390874489","https://openalex.org/W4391617291","https://openalex.org/W4392931989","https://openalex.org/W4396903901","https://openalex.org/W4399431287","https://openalex.org/W4401880879","https://openalex.org/W4401943317","https://openalex.org/W4402952463","https://openalex.org/W4403217981","https://openalex.org/W4403759418","https://openalex.org/W4404125046","https://openalex.org/W4405717605","https://openalex.org/W4413303322","https://openalex.org/W4415795885","https://openalex.org/W4415800770","https://openalex.org/W7160175629"],"related_works":[],"abstract_inverted_index":{"A":[0,69],"conventional":[1,32],"camera":[2],"captures":[3],"an":[4,216],"image":[5,24],"of":[6,22,42,67,71,131,169],"a":[7,27,109,113,126,140,179,207],"directly":[8,44],"visible":[9,45],"scene":[10,29,174],"by":[11,162,177,202,212],"measuring":[12],"the":[13,35,43,64,88,119,151,165,184],"light":[14,37,56],"intensity":[15,57],"(and":[16],"color)":[17],"arriving":[18],"at":[19,255],"each":[20],"pixel":[21],"its":[23],"sensor":[25],"from":[26],"corresponding":[28],"patch.":[30],"Accordingly,":[31],"photography":[33],"treats":[34],"measured":[36,55],"as":[38,245],"being":[39],"solely":[40,230],"informative":[41,84],"scene.":[46],"Recent":[47],"research":[48],"has":[49],"shown":[50],"that":[51,97,159,197],"subtle":[52],"variations":[53],"in":[54,123,149],"can":[58],"carry":[59],"information":[60],"about":[61],"scenes":[62],"outside":[63],"camera\u2019s":[65],"line":[66],"sight.":[68],"subset":[70],"these":[72],"methods":[73,158],"exploits":[74],"preexisting":[75],"obstructions\u2014i.e.,":[76],"occluders\u2014":[77],"which":[78,150],"cast":[79],"barely":[80],"perceptible":[81],"yet":[82],"highly":[83],"soft":[85],"shadows":[86],"onto":[87],"observed":[89],"planar":[90],"surface.":[91],"Whereas":[92],"most":[93],"prior":[94],"works":[95],"assume":[96],"exploitable":[98],"occluders":[99],"are":[100,253],"either":[101],"partly":[102],"or":[103,106],"wholly":[104],"known":[105],"almost":[107],"planar,":[108],"recent":[110,156],"work":[111,138],"blended":[112],"trained":[114,142,229],"diffusion-based":[115,209],"sampler":[116],"to":[117,206,215,236],"reconstruct":[118],"hidden":[120,134,173],"occluding":[121,170,185],"structures":[122],"3D":[124],"with":[125],"transverse":[127],"2D":[128],"radiosity":[129],"map":[130],"all":[132],"other":[133],"non-occluding":[135,172,187],"structures.":[136,175],"This":[137],"proposes":[139],"fully":[141],"novel":[143],"multipath":[144,153],"decoding":[145],"UNet":[146],"(MDUNet)":[147],"architecture,":[148],"multimodal,":[152],"decoder":[154],"parallels":[155],"physics-based":[157],"achieve":[160],"success":[161],"explicitly":[163],"separating":[164],"representations":[166],"and":[167,171,186,211,242,250],"reconstructions":[168],"However,":[176],"sharing":[178],"latent":[180],"feature":[181],"representation":[182],"between":[183],"structures,":[188],"MDUNet":[189,198,227],"couples":[190],"their":[191],"reconstruction":[192,223],"pathways.":[193],"Empirical":[194],"results":[195],"show":[196],"improves":[199],"inference":[200],"times":[201],"over":[203],"100\u00d7":[204],"compared":[205,214],"state-of-the-art":[208],"method":[210],"1000\u00d7":[213],"iterative":[217],"optimization-based":[218],"method,":[219],"while":[220],"also":[221],"improving":[222],"quality.":[224],"In":[225],"addition,":[226],"is":[228],"on":[231],"simulation":[232],"data":[233,252],"but":[234],"generalizes":[235],"real":[237],"experimental":[238],"data,":[239],"maintaining":[240],"accuracy":[241],"stability":[243],"even":[244],"ambient":[246],"illumination":[247],"increases.":[248],"Code":[249],"training":[251],"available":[254],"https://github.com/Predstan/MDUNet.":[256]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-05-06T00:00:00"}