{"id":"https://openalex.org/W4417081719","doi":"https://doi.org/10.48550/arxiv.2509.14641","title":"TriLift: Interpolation-Free Tri-Plane Lifting for Efficient 3D Perception on Embedded Systems","display_name":"TriLift: Interpolation-Free Tri-Plane Lifting for Efficient 3D Perception on Embedded Systems","publication_year":2025,"publication_date":"2025-09-18","ids":{"openalex":"https://openalex.org/W4417081719","doi":"https://doi.org/10.48550/arxiv.2509.14641"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.14641","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.14641","pdf_url":"https://arxiv.org/pdf/2509.14641","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.14641","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068381331","display_name":"Sibaek Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Sibaek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114138141","display_name":"Jiung Yeon","orcid":"https://orcid.org/0009-0004-7649-549X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeon, Jiung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060844581","display_name":"Hyeonwoo Yu","orcid":"https://orcid.org/0000-0002-9505-7581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Hyeonwoo","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":true,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.4146000146865845,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.4146000146865845,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.32429999113082886,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.05950000137090683,"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/volume","display_name":"Volume (thermodynamics)","score":0.593500018119812},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5845999717712402},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.5551999807357788},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5297999978065491},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.41909998655319214},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4081999957561493},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.37119999527931213},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3578999936580658}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6565999984741211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018000245094299},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.593500018119812},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5388000011444092},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5297999978065491},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.41909998655319214},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3197000026702881},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2509.14641","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.14641","pdf_url":"https://arxiv.org/pdf/2509.14641","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:arXiv.org:2509.14641","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2509.14641","pdf_url":"https://arxiv.org/pdf/2509.14641","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.14641","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.14641","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":"pmh:oai:arXiv.org:2509.14641","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.14641","pdf_url":"https://arxiv.org/pdf/2509.14641","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Dense":[0],"3D":[1,41,61],"convolutions":[2],"provide":[3],"high":[4],"accuracy":[5,173],"for":[6,13,39,222],"perception":[7],"but":[8],"are":[9],"too":[10],"computationally":[11,35],"expensive":[12],"real-time":[14,214],"robotic":[15,224],"systems.":[16],"Existing":[17],"tri-plane":[18,52],"methods":[19],"rely":[20],"on":[21,159,206],"2D":[22,79],"image":[23],"features":[24,65,132],"with":[25,115,129,197],"interpolation,":[26],"point-wise":[27],"queries,":[28],"and":[29,37,54,66,73,145,163,165,172,180,188],"implicit":[30],"MLPs,":[31],"which":[32],"makes":[33],"them":[34],"heavy":[36],"unsuitable":[38],"embedded":[40,223],"inference.":[42],"As":[43],"an":[44,207],"alternative,":[45],"we":[46,95,122,156,166],"propose":[47],"TriLift,":[48],"a":[49,68,97,124,134,139,191,199],"novel":[50],"interpolation-free":[51],"lifting":[53],"volumetric":[55,126],"fusion":[56],"framework":[57],"that":[58,103,141,178],"directly":[59],"projects":[60],"voxels":[62],"into":[63],"plane":[64],"reconstructs":[67],"feature":[69],"volume":[70],"through":[71,133],"broadcast":[72],"summation.":[74],"This":[75],"shifts":[76],"nonlinearity":[77],"to":[78],"convolutions,":[80],"reducing":[81,194],"complexity":[82],"while":[83,186],"remaining":[84],"fully":[85],"parallelizable.":[86],"To":[87,118,148],"mitigate":[88],"spatial":[89,107],"information":[90,108],"loss":[91],"inherent":[92],"in":[93,202],"projections,":[94],"incorporate":[96],"lightweight":[98,135],"adaptive":[99],"positional":[100],"encoding":[101],"module":[102],"helps":[104],"bridge":[105],"the":[106,130,150,153,168,217,220],"gap,":[109],"dynamically":[110],"recovering":[111],"fine":[112],"geometric":[113],"details":[114],"negligible":[116],"overhead.":[117],"capture":[119],"global":[120],"context,":[121],"add":[123],"low-resolution":[125],"branch":[127],"fused":[128],"lifted":[131],"integration":[136],"layer,":[137],"yielding":[138],"design":[140],"is":[142],"both":[143],"efficient":[144],"end-to-end":[146],"GPU-accelerated.":[147],"validate":[149],"effectiveness":[151],"of":[152,219],"proposed":[154],"method,":[155],"conduct":[157],"experiments":[158],"classification,":[160],"completion,":[161],"segmentation,":[162],"detection,":[164],"map":[167],"trade-off":[169],"between":[170],"efficiency":[171],"across":[174],"tasks.":[175],"Results":[176],"show":[177,190],"classification":[179],"completion":[181],"retain":[182],"or":[183],"improve":[184],"accuracy,":[185],"segmentation":[187],"detection":[189],"trade-off,":[192],"significantly":[193],"computational":[195],"demand":[196],"only":[198],"slight":[200],"decrease":[201],"accuracy.":[203],"On-device":[204],"benchmarks":[205],"NVIDIA":[208],"Jetson":[209],"Orin":[210],"Nano":[211],"confirm":[212],"robust":[213],"throughput,":[215],"demonstrating":[216],"suitability":[218],"approach":[221],"perception.":[225]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}