{"id":"https://openalex.org/W4417458764","doi":"https://doi.org/10.48550/arxiv.2512.13411","title":"Computer vision training dataset generation for robotic environments using Gaussian splatting","display_name":"Computer vision training dataset generation for robotic environments using Gaussian splatting","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W4417458764","doi":"https://doi.org/10.48550/arxiv.2512.13411"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.13411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13411","pdf_url":"https://arxiv.org/pdf/2512.13411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.13411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120843755","display_name":"Patryk Ni\u017ceniec","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni\u017ceniec, Patryk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088595367","display_name":"Marcin Iwanowski","orcid":"https://orcid.org/0000-0001-8347-1112"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iwanowski, Marcin","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":false,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3950999975204468,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3950999975204468,"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":0.10679999738931656,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.06560000032186508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6215000152587891},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.567799985408783},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5273000001907349},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4293999969959259},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4034999907016754},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3889999985694885},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.383899986743927},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.37130001187324524},{"id":"https://openalex.org/keywords/graphics-pipeline","display_name":"Graphics pipeline","score":0.3659000098705292},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3553999960422516}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8219000101089478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7407000064849854},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6919000148773193},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6215000152587891},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.567799985408783},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4293999969959259},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4034999907016754},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.4011000096797943},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C173552908","wikidata":"https://www.wikidata.org/wiki/Q1366289","display_name":"Graphics pipeline","level":4,"score":0.3659000098705292},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C50637493","wikidata":"https://www.wikidata.org/wiki/Q1136781","display_name":"Morphing","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C190390380","wikidata":"https://www.wikidata.org/wiki/Q62505","display_name":"Physics engine","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C44185422","wikidata":"https://www.wikidata.org/wiki/Q6002064","display_name":"Image-based modeling and rendering","level":3,"score":0.2906999886035919},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.28139999508857727},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.27900001406669617},{"id":"https://openalex.org/C116921373","wikidata":"https://www.wikidata.org/wiki/Q2816483","display_name":"Real-time rendering","level":3,"score":0.27630001306533813},{"id":"https://openalex.org/C30769735","wikidata":"https://www.wikidata.org/wiki/Q2165951","display_name":"Volume rendering","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.27059999108314514},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C36816356","wikidata":"https://www.wikidata.org/wiki/Q16911860","display_name":"3D rendering","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2529999911785126},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.13411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13411","pdf_url":"https://arxiv.org/pdf/2512.13411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2512.13411","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.13411","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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:2512.13411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.13411","pdf_url":"https://arxiv.org/pdf/2512.13411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,93],"paper":[1],"introduces":[2],"a":[3,66,86,135,140,147],"novel":[4],"pipeline":[5],"for":[6,15,122,167],"generating":[7],"large-scale,":[8],"highly":[9],"realistic,":[10],"and":[11,34,37,58,108,120,158,171],"automatically":[12,119],"labeled":[13],"datasets":[14],"computer":[16],"vision":[17],"tasks":[18],"in":[19,65],"robotic":[20],"environments.":[21],"Our":[22,131],"approach":[23],"addresses":[24],"the":[25,29,38,55,81,100,155],"critical":[26],"challenges":[27],"of":[28,41,54,83,143,150],"domain":[30],"gap":[31],"between":[32],"synthetic":[33,152],"real-world":[35],"imagery":[36],"time-consuming":[39],"bottleneck":[40],"manual":[42],"annotation.":[43],"We":[44],"leverage":[45],"3D":[46],"Gaussian":[47],"Splatting":[48],"(3DGS)":[49],"to":[50,102],"create":[51,72],"photorealistic":[52],"representations":[53],"operational":[56],"environment":[57],"objects.":[59],"These":[60],"assets":[61],"are":[62,117],"then":[63,96],"used":[64],"game":[67],"engine":[68],"where":[69],"physics":[70],"simulations":[71],"natural":[73],"arrangements.":[74],"A":[75],"novel,":[76],"two-pass":[77],"rendering":[78],"technique":[79],"combines":[80],"realism":[82],"splats":[84],"with":[85,99,125,146],"shadow":[87],"map":[88,94],"generated":[89,118],"from":[90],"proxy":[91],"meshes.":[92],"is":[95],"algorithmically":[97],"composited":[98],"image":[101],"add":[103],"both":[104],"physically":[105],"plausible":[106],"shadows":[107],"subtle":[109],"highlights,":[110],"significantly":[111],"enhancing":[112],"realism.":[113],"Pixel-perfect":[114],"segmentation":[115,159],"masks":[116],"formatted":[121],"direct":[123],"use":[124],"object":[126],"detection":[127,157],"models":[128],"like":[129],"YOLO.":[130],"experiments":[132],"show":[133],"that":[134],"hybrid":[136],"training":[137],"strategy,":[138],"combining":[139],"small":[141],"set":[142],"real":[144],"images":[145],"large":[148],"volume":[149],"our":[151],"data,":[153],"yields":[154],"best":[156],"performance,":[160],"confirming":[161],"this":[162],"as":[163],"an":[164],"optimal":[165],"strategy":[166],"efficiently":[168],"achieving":[169],"robust":[170],"accurate":[172],"models.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-12-17T00:00:00"}
