{"id":"https://openalex.org/W7131139605","doi":"https://doi.org/10.1109/iccvw69036.2025.00280","title":"DeepCollide: Scalable Data-Driven High DoF Configuration Space Modeling Using Implicit Neural Representations","display_name":"DeepCollide: Scalable Data-Driven High DoF Configuration Space Modeling Using Implicit Neural Representations","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131139605","doi":"https://doi.org/10.1109/iccvw69036.2025.00280"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5126582839","display_name":"Gabriel Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gabriel Guo","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014731328","display_name":"Judah Goldfeder","orcid":"https://orcid.org/0009-0004-3892-7079"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Judah Goldfeder","raw_affiliation_strings":["Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101285239","display_name":"Aniv Ray","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aniv Ray","raw_affiliation_strings":["Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067968172","display_name":"Tony Dear","orcid":"https://orcid.org/0000-0001-5780-3240"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tony Dear","raw_affiliation_strings":["Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125051701","display_name":"Hod Lipson","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hod Lipson","raw_affiliation_strings":["Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2689","last_page":"2699"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.07689999788999557,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.07689999788999557,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.07100000232458115,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06769999861717224,"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/collision-detection","display_name":"Collision detection","score":0.8069999814033508},{"id":"https://openalex.org/keywords/workspace","display_name":"Workspace","score":0.7249000072479248},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6478000283241272},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.6358000040054321},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6136999726295471},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5656999945640564},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.43709999322891235},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4207000136375427},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.37779998779296875}],"concepts":[{"id":"https://openalex.org/C199668693","wikidata":"https://www.wikidata.org/wiki/Q1550329","display_name":"Collision detection","level":3,"score":0.8069999814033508},{"id":"https://openalex.org/C58581272","wikidata":"https://www.wikidata.org/wiki/Q12741163","display_name":"Workspace","level":3,"score":0.7249000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.652400016784668},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6478000283241272},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.6358000040054321},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6136999726295471},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5656999945640564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.43709999322891235},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37450000643730164},{"id":"https://openalex.org/C90738871","wikidata":"https://www.wikidata.org/wiki/Q41642869","display_name":"Configuration space","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.3319000005722046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C104065381","wikidata":"https://www.wikidata.org/wiki/Q1002535","display_name":"Geometric modeling","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C182124507","wikidata":"https://www.wikidata.org/wiki/Q166154","display_name":"Line segment","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.274399995803833},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2741999924182892},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C29123130","wikidata":"https://www.wikidata.org/wiki/Q874709","display_name":"Computational geometry","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W181534116","https://openalex.org/W1538281461","https://openalex.org/W1554588678","https://openalex.org/W1660808159","https://openalex.org/W1971632690","https://openalex.org/W1999250663","https://openalex.org/W2004139909","https://openalex.org/W2079872371","https://openalex.org/W2101234009","https://openalex.org/W2110771015","https://openalex.org/W2122111042","https://openalex.org/W2128990851","https://openalex.org/W2138550851","https://openalex.org/W2138706770","https://openalex.org/W2141664020","https://openalex.org/W2152686676","https://openalex.org/W2152869188","https://openalex.org/W2163178194","https://openalex.org/W2163228497","https://openalex.org/W2165558283","https://openalex.org/W2166316739","https://openalex.org/W2172108647","https://openalex.org/W2346637437","https://openalex.org/W2409044193","https://openalex.org/W2738195924","https://openalex.org/W2910081881","https://openalex.org/W2954761708","https://openalex.org/W2963446712","https://openalex.org/W2963627347","https://openalex.org/W2963926543","https://openalex.org/W3003604824","https://openalex.org/W3009220630","https://openalex.org/W3035965352","https://openalex.org/W3108650437","https://openalex.org/W3131058854","https://openalex.org/W3185327196","https://openalex.org/W4200150166","https://openalex.org/W4210374360","https://openalex.org/W4239510810","https://openalex.org/W4280582276","https://openalex.org/W4285085664","https://openalex.org/W4386076276"],"related_works":[],"abstract_inverted_index":{"Collision":[0],"detection":[1,13,29,74],"is":[2,37],"essential":[3],"to":[4,25,45,99,105,115],"virtually":[5],"all":[6],"robotics":[7],"applications.":[8],"However,":[9],"traditional":[10],"geometric":[11,35],"collision":[12,28,73,78],"methods":[14],"generally":[15],"require":[16],"pre-existing":[17],"workspace":[18,119],"geometry":[19],"representations;":[20],"thus,":[21],"they":[22],"are":[23],"unable":[24],"infer":[26],"the":[27,72,94,113],"function":[30,75],"from":[31,76],"sampled":[32,77],"data":[33,107],"when":[34],"information":[36],"unavailable.":[38],"Additionally,":[39],"their":[40],"speed":[41],"often":[42],"scales":[43],"unfavorably":[44],"higher-DoF":[46],"and/or":[47],"obstacledense":[48],"systems.":[49],"End-to-end":[50],"learning-based":[51],"approaches":[52],"can":[53],"overcome":[54],"these":[55],"limitations.":[56],"Following":[57],"this":[58],"line":[59],"of":[60],"research,":[61],"we":[62],"present":[63],"DeepCollide,":[64],"an":[65],"implicit":[66],"neural":[67],"representation":[68],"method":[69],"for":[70],"approximating":[71],"data.":[79],"As":[80],"shown":[81],"by":[82],"our":[83],"theoretical":[84],"analysis":[85],"and":[86,108],"empirical":[87],"evidence,":[88],"DeepCollide":[89],"presents":[90],"clear":[91],"benefits":[92],"over":[93],"state-of-the-art,":[95],"as":[96,110,112],"it":[97],"relates":[98],"time":[100],"cost":[101],"scalability":[102],"with":[103],"respect":[104],"training":[106],"DoF,":[109],"well":[111],"ability":[114],"accurately":[116],"express":[117],"complex":[118],"geometries.":[120]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-24T00:00:00"}
