{"id":"https://openalex.org/W3151631238","doi":"https://doi.org/10.1109/icra48506.2021.9561327","title":"Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks","display_name":"Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks","publication_year":2021,"publication_date":"2021-05-30","ids":{"openalex":"https://openalex.org/W3151631238","doi":"https://doi.org/10.1109/icra48506.2021.9561327","mag":"3151631238"},"language":"en","primary_location":{"id":"doi:10.1109/icra48506.2021.9561327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9561327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.15226","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104084167","display_name":"Siddharth Srivastava","orcid":"https://orcid.org/0009-0009-5667-8287"},"institutions":[{"id":"https://openalex.org/I1331500379","display_name":"Centre for Development of Advanced Computing","ror":"https://ror.org/022abst40","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1331500379","https://openalex.org/I4210121746"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Siddharth Srivastava","raw_affiliation_strings":["Centre for Development of Advanced Computing,Noida,India","Centre For Development of Advanced Computing, Noida, India"],"affiliations":[{"raw_affiliation_string":"Centre for Development of Advanced Computing,Noida,India","institution_ids":["https://openalex.org/I1331500379"]},{"raw_affiliation_string":"Centre For Development of Advanced Computing, Noida, India","institution_ids":["https://openalex.org/I1331500379"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100705959","display_name":"Gaurav Sharma","orcid":"https://orcid.org/0000-0001-9735-9519"},"institutions":[{"id":"https://openalex.org/I94234084","display_name":"Indian Institute of Technology Kanpur","ror":"https://ror.org/05pjsgx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I94234084"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gaurav Sharma","raw_affiliation_strings":["TensorTour and IIT Kanpur"],"affiliations":[{"raw_affiliation_string":"TensorTour and IIT Kanpur","institution_ids":["https://openalex.org/I94234084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104084167"],"corresponding_institution_ids":["https://openalex.org/I1331500379"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05164673,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"12903","last_page":"12909"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9969000220298767,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8816307783126831},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.706346333026886},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.604268491268158},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.5299357771873474},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5126851797103882},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47457221150398254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4528343677520752},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.451140433549881},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37733832001686096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.350686252117157}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8816307783126831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.706346333026886},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.604268491268158},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.5299357771873474},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5126851797103882},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47457221150398254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4528343677520752},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.451140433549881},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37733832001686096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.350686252117157}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icra48506.2021.9561327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9561327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.15226","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.15226","pdf_url":"https://arxiv.org/pdf/2103.15226","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3151631238","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2103.15226","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2103.15226","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.15226","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.15226","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.15226","pdf_url":"https://arxiv.org/pdf/2103.15226","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3151631238.pdf","grobid_xml":"https://content.openalex.org/works/W3151631238.grobid-xml"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W1920022804","https://openalex.org/W1983670955","https://openalex.org/W2003067321","https://openalex.org/W2024039087","https://openalex.org/W2087063852","https://openalex.org/W2117002552","https://openalex.org/W2211722331","https://openalex.org/W2460657278","https://openalex.org/W2553307952","https://openalex.org/W2560609797","https://openalex.org/W2563189218","https://openalex.org/W2594519801","https://openalex.org/W2604249033","https://openalex.org/W2606987267","https://openalex.org/W2612326916","https://openalex.org/W2755126795","https://openalex.org/W2902302021","https://openalex.org/W2916443959","https://openalex.org/W2917964190","https://openalex.org/W2930709109","https://openalex.org/W2938428612","https://openalex.org/W2939201152","https://openalex.org/W2939221413","https://openalex.org/W2942498895","https://openalex.org/W2950493473","https://openalex.org/W2958885478","https://openalex.org/W2962928871","https://openalex.org/W2963125977","https://openalex.org/W2963509914","https://openalex.org/W2964228567","https://openalex.org/W2964253930","https://openalex.org/W2964257316","https://openalex.org/W2968397098","https://openalex.org/W2968474279","https://openalex.org/W2976081675","https://openalex.org/W2979750740","https://openalex.org/W2981199548","https://openalex.org/W2990045899","https://openalex.org/W2990613095","https://openalex.org/W2997216793","https://openalex.org/W3007809903","https://openalex.org/W3008249013","https://openalex.org/W3012227311","https://openalex.org/W3014589305","https://openalex.org/W3014902535","https://openalex.org/W3034239841","https://openalex.org/W3034591723","https://openalex.org/W3035472394","https://openalex.org/W3035536783","https://openalex.org/W3041660378","https://openalex.org/W3103044747","https://openalex.org/W3107479685","https://openalex.org/W3107518100","https://openalex.org/W3109646990","https://openalex.org/W3110503160","https://openalex.org/W3174856432","https://openalex.org/W4210257598","https://openalex.org/W6640300118","https://openalex.org/W6739778489","https://openalex.org/W6743978190","https://openalex.org/W6761590077","https://openalex.org/W6763422710","https://openalex.org/W6766842342","https://openalex.org/W6768349772","https://openalex.org/W6780045877","https://openalex.org/W6781726263","https://openalex.org/W6807384801"],"related_works":["https://openalex.org/W3205147687","https://openalex.org/W2963312728","https://openalex.org/W3001210208","https://openalex.org/W3092049690","https://openalex.org/W2989599677","https://openalex.org/W3195439460","https://openalex.org/W3045020488","https://openalex.org/W3102456204","https://openalex.org/W2892122533","https://openalex.org/W3123767042","https://openalex.org/W3095213891","https://openalex.org/W2805090752","https://openalex.org/W2563860824","https://openalex.org/W2947078341","https://openalex.org/W2886034153","https://openalex.org/W3161483374","https://openalex.org/W3179161883","https://openalex.org/W3036542036","https://openalex.org/W2907101105","https://openalex.org/W3162272824"],"abstract_inverted_index":{"We":[0,86,210],"propose":[1,33,59],"simple":[2],"yet":[3],"effective":[4],"improvements":[5],"in":[6,94,96,103,120],"point":[7,25,69,158],"representations":[8,38],"and":[9,117,202],"local":[10,41,83],"neighborhood":[11,84],"graph":[12,19,63],"construction":[13,64],"within":[14],"the":[15,36,45,62,82,107,124,148,153,183,189,214],"general":[16,132,149],"framework":[17],"of":[18,44,98,106,156,188],"neural":[20],"networks":[21],"(GNNs)":[22],"for":[23,65,67,80,194,226],"3D":[24,68,157,195,206],"cloud":[26],"processing.":[27],"As":[28,54,123],"a":[29,52,55,76],"first":[30],"contribution,":[31,57],"we":[32,58,141,163],"to":[34,60,92,113,129,151],"augment":[35],"vertex":[37],"with":[39,75,131,165,169,177],"important":[40],"geometric":[42,139,154],"information":[43],"points,":[46],"followed":[47],"by":[48,101],"nonlinear":[49],"projection":[50],"using":[51],"MLP.":[53],"second":[56],"improve":[61,118],"GNNs":[66,126],"clouds.":[70,159],"The":[71,109,228],"existing":[72],"methods":[73,111],"work":[74,130],"k-NN":[77],"based":[78],"approach":[79],"constructing":[81],"graph.":[85],"argue":[87],"that":[88,182,213],"it":[89],"might":[90],"lead":[91],"reduction":[93],"coverage":[95,119],"case":[97],"dense":[99],"sampling":[100],"sensors":[102],"some":[104],"regions":[105],"scene.":[108],"proposed":[110,184,215],"aims":[112],"counter":[114],"such":[115,121],"problems":[116],"cases.":[122],"traditional":[125],"were":[127],"designed":[128],"graphs,":[133],"where":[134],"vertices":[135],"may":[136],"have":[137],"no":[138],"interpretations,":[140],"see":[142],"both":[143],"our":[144],"proposals":[145],"as":[146,174,176],"augmenting":[147],"graphs":[150],"incorporate":[152],"nature":[155],"While":[160],"being":[161],"simple,":[162],"demonstrate":[164],"multiple":[166],"challenging":[167],"benchmarks,":[168],"relatively":[170],"clean":[171],"CAD":[172],"models,":[173],"well":[175],"real":[178],"world":[179],"noisy":[180],"scans,":[181],"method":[185],"achieves":[186,217],"state":[187],"art":[190],"results":[191],"on":[192],"benchmarks":[193],"classification":[196],"(ModelNet40)":[197],",":[198],"part":[199],"segmentation":[200,204],"(ShapeNet)":[201],"semantic":[203],"(Stanford":[205],"Indoor":[207],"Scenes":[208],"Dataset).":[209],"also":[211],"show":[212],"network":[216],"faster":[218],"training":[219],"convergence,":[220],"i.e.":[221],"\u223c":[222],"40%":[223],"less":[224],"epochs":[225],"classification.":[227],"project":[229],"details":[230],"are":[231],"available":[232],"at":[233],"https://siddharthsrivastava.github.io/publication/geomgcnn/":[234]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
