{"id":"https://openalex.org/W2968591670","doi":"https://doi.org/10.1109/icra.2019.8794264","title":"Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks","display_name":"Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2968591670","doi":"https://doi.org/10.1109/icra.2019.8794264","mag":"2968591670"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2019.8794264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2019.8794264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Robotics and Automation (ICRA)","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/A5049344283","display_name":"Chenxi Tu","orcid":"https://orcid.org/0000-0003-0769-4407"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chenxi Tu","raw_affiliation_strings":["Department of Intelligent System, Graduate School of Informatics, Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Intelligent System, Graduate School of Informatics, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113688124","display_name":"Eijiro Takeuchi","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Eijiro Takeuchi","raw_affiliation_strings":["Department of Intelligent System, Graduate School of Informatics, Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Intelligent System, Graduate School of Informatics, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037312161","display_name":"Alexander Carballo","orcid":"https://orcid.org/0000-0002-5941-2195"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Alexander Carballo","raw_affiliation_strings":["Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042118446","display_name":"Kazuya Takeda","orcid":"https://orcid.org/0000-0002-0330-1787"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuya Takeda","raw_affiliation_strings":["Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5049344283"],"corresponding_institution_ids":["https://openalex.org/I60134161"],"apc_list":null,"apc_paid":null,"fwci":4.5928,"has_fulltext":false,"cited_by_count":84,"citation_normalized_percentile":{"value":0.95867274,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3274","last_page":"3280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9995999932289124,"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":0.9995999932289124,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9991999864578247,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8868739604949951},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.764796257019043},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7372588515281677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7003539204597473},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6793421506881714},{"id":"https://openalex.org/keywords/octree","display_name":"Octree","score":0.553964376449585},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.5405461192131042},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5192785263061523},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4919838607311249},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.41573092341423035},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.36012768745422363},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18585553765296936},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14270877838134766}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8868739604949951},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.764796257019043},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7372588515281677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7003539204597473},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6793421506881714},{"id":"https://openalex.org/C141297171","wikidata":"https://www.wikidata.org/wiki/Q1143237","display_name":"Octree","level":2,"score":0.553964376449585},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.5405461192131042},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5192785263061523},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4919838607311249},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.41573092341423035},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.36012768745422363},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18585553765296936},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14270877838134766},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2019.8794264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2019.8794264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Robotics and Automation (ICRA)","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":45,"referenced_works":["https://openalex.org/W242309356","https://openalex.org/W355554815","https://openalex.org/W1495194439","https://openalex.org/W1529450504","https://openalex.org/W1950970151","https://openalex.org/W1989305571","https://openalex.org/W1997917718","https://openalex.org/W2025768430","https://openalex.org/W2066005785","https://openalex.org/W2096143401","https://openalex.org/W2100495367","https://openalex.org/W2115650263","https://openalex.org/W2121272499","https://openalex.org/W2137985019","https://openalex.org/W2140055066","https://openalex.org/W2187463565","https://openalex.org/W2194775991","https://openalex.org/W2206222117","https://openalex.org/W2217530659","https://openalex.org/W2302255633","https://openalex.org/W2343938449","https://openalex.org/W2396976214","https://openalex.org/W2401640538","https://openalex.org/W2476548250","https://openalex.org/W2532629912","https://openalex.org/W2541674938","https://openalex.org/W2567458831","https://openalex.org/W2741008920","https://openalex.org/W2741653487","https://openalex.org/W2755280892","https://openalex.org/W2952626150","https://openalex.org/W2963147844","https://openalex.org/W2963149687","https://openalex.org/W2963372104","https://openalex.org/W2963410064","https://openalex.org/W2963470893","https://openalex.org/W4294554810","https://openalex.org/W6629469057","https://openalex.org/W6631686956","https://openalex.org/W6680455289","https://openalex.org/W6704734575","https://openalex.org/W6712185225","https://openalex.org/W6713563955","https://openalex.org/W6729059855","https://openalex.org/W6731928466"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W4293094720","https://openalex.org/W2739701376","https://openalex.org/W2392034603","https://openalex.org/W2352029666","https://openalex.org/W4200108838"],"abstract_inverted_index":{"The":[0,21],"use":[1],"of":[2,25,47,95,101,112,220],"3D":[3,30,96,149],"LiDAR,":[4],"which":[5,133],"has":[6,33],"proven":[7],"its":[8],"capabilities":[9],"in":[10,37,80,107,126,189,224],"autonomous":[11],"driving":[12],"systems,":[13],"is":[14,51,65,105],"now":[15],"expanding":[16],"into":[17,57],"many":[18],"other":[19],"fields.":[20],"sharing":[22],"and":[23,44,88,139,159,206],"transmission":[24],"point":[26,162,183,196],"cloud":[27,163,184,197],"data":[28,75,86,124,198],"from":[29,76,148],"LiDAR":[31,68,97,123],"sensors":[32],"broad":[34],"application":[35,180],"prospects":[36],"robotics.":[38],"However,":[39],"due":[40],"to":[41,53,91,109,117,142,152],"the":[42,73,85,92,99,102,166,175,221],"sparseness":[43],"disorderly":[45],"nature":[46],"this":[48,119,127,190],"data,":[49,98],"it":[50,55],"difficult":[52],"compress":[54,118,144],"directly":[56],"a":[58,81,131,135,214],"very":[59],"low":[60],"volume.":[61],"A":[62],"potential":[63,218],"solution":[64],"utilizing":[66],"raw":[67,74],"data.":[69],"We":[70,192],"can":[71,199],"rearrange":[72],"each":[77],"frame":[78],"losslessly":[79],"2D":[82,103,110,121],"matrix,":[83],"making":[84],"compact":[87],"orderly.":[89],"Due":[90],"special":[93],"structure":[94],"texture":[100],"matrix":[104],"irregular,":[106],"contrast":[108],"matrices":[111],"camera":[113],"images.":[114],"In":[115],"order":[116],"raw,":[120],"formatted":[122],"efficiently,":[125],"paper":[128],"we":[129],"propose":[130],"method":[132,158,223],"uses":[134,219],"recurrent":[136],"neural":[137],"network":[138],"residual":[140],"blocks":[141],"progressively":[143],"one":[145],"frame's":[146],"information":[147],"LiDAR.":[150],"Compared":[151],"our":[153],"previous":[154],"image":[155],"compression":[156,164,185],"based":[157],"generic":[160],"octree":[161],"method,":[165],"proposed":[167,222],"approach":[168],"needs":[169],"much":[170],"less":[171],"volume":[172],"while":[173],"giving":[174],"same":[176],"decompression":[177],"accuracy.":[178],"Potential":[179],"scenarios":[181],"for":[182,211],"are":[186],"also":[187],"considered":[188],"paper.":[191],"describe":[193],"how":[194],"decompressed":[195],"be":[200],"used":[201],"with":[202],"SLAM":[203],"(simultaneous":[204],"localization":[205,212],"mapping)":[207],"as":[208,210],"well":[209],"using":[213],"given":[215],"map,":[216],"illustrating":[217],"real":[225],"robotics":[226],"applications.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":3}],"updated_date":"2026-05-26T13:28:51.108037","created_date":"2025-10-10T00:00:00"}
