{"id":"https://openalex.org/W7140291210","doi":"https://doi.org/10.48550/arxiv.2603.22792","title":"Instrument-Splatting++: Towards Controllable Surgical Instrument Digital Twin Using Gaussian Splatting","display_name":"Instrument-Splatting++: Towards Controllable Surgical Instrument Digital Twin Using Gaussian Splatting","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140291210","doi":"https://doi.org/10.48550/arxiv.2603.22792"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22792","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22792","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013977668","display_name":"Shuojue Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Shuojue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130596398","display_name":"Zijian Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zijian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130605635","display_name":"Chengjiaao Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Chengjiaao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130576024","display_name":"Qian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111350501","display_name":"Daiyun Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Daiyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058834100","display_name":"Chang Han Low","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Low, Chang Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130604639","display_name":"Septimiu E. Salcudean","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salcudean, Septimiu E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130553983","display_name":"Yueming Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Yueming","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/T10916","display_name":"Surgical Simulation and Training","score":0.8604999780654907,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10916","display_name":"Surgical Simulation and Training","score":0.8604999780654907,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10868","display_name":"Soft Robotics and Applications","score":0.039500001817941666,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.019300000742077827,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5620999932289124},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5458999872207642},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4729999899864197},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.3894999921321869},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.37929999828338623},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.37860000133514404},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.37310001254081726},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3684000074863434},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.3659999966621399},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3637999892234802}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7421000003814697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7396000027656555},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6547999978065491},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5458999872207642},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4729999899864197},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.37929999828338623},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3637999892234802},{"id":"https://openalex.org/C173552908","wikidata":"https://www.wikidata.org/wiki/Q1366289","display_name":"Graphics pipeline","level":4,"score":0.36149999499320984},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3077000081539154},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.2906999886035919},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.27649998664855957},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.25270000100135803},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22792","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":"doi:10.48550/arxiv.2603.22792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22792","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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"High-quality":[0],"and":[1,23,66,85,94,113,131,148,165,173],"controllable":[2,46,194],"digital":[3],"twins":[4],"of":[5],"surgical":[6,41],"instruments":[7,42],"are":[8],"critical":[9],"for":[10],"Real2Sim":[11],"in":[12],"robot-assisted":[13],"surgery,":[14],"as":[15,43],"they":[16],"enable":[17],"realistic":[18,150],"simulation,":[19],"synthetic":[20,108],"data":[21,190],"generation,":[22],"perception":[24],"learning":[25],"under":[26],"novel":[27],"poses.":[28],"We":[29,155,180],"present":[30],"Instrument-Splatting++,":[31],"a":[32,44,81,102,114,183],"monocular":[33],"3D":[34],"Gaussian":[35,47,64,196],"Splatting":[36],"(3DGS)":[37],"framework":[38,143],"that":[39,59],"reconstructs":[40],"fully":[45],"asset":[48],"with":[49,55,70],"high":[50],"fidelity.":[51],"Our":[52],"pipeline":[53],"starts":[54],"part-wise":[56],"geometry":[57],"pretraining":[58],"injects":[60],"CAD":[61],"priors":[62],"into":[63],"primitives":[65],"equips":[67],"the":[68,76],"representation":[69],"part-aware":[71],"semantic":[72],"rendering.":[73],"Built":[74],"on":[75,159],"pretrained":[77],"model,":[78],"we":[79,121],"propose":[80],"semantics-aware":[82],"pose":[83,93,129,136,146],"estimation":[84,147],"tracking":[86],"(SAPET)":[87],"method":[88,158],"to":[89],"recover":[90],"per-frame":[91],"6-DoF":[92],"joint":[95],"angles":[96],"from":[97,107,152,162,192],"unposed":[98,153],"endoscopic":[99],"videos,":[100],"where":[101,188],"gripper-tip":[103],"network":[104],"trained":[105],"purely":[106],"semantics":[109],"provides":[110],"robust":[111,132],"supervision":[112],"loose":[115],"regularization":[116],"suppresses":[117],"singular":[118],"articulations.":[119],"Finally,":[120],"introduce":[122],"Robust":[123],"Texture":[124],"Learning":[125],"(RTL),":[126],"which":[127],"alternates":[128],"refinement":[130],"appearance":[133],"optimization,":[134],"mitigating":[135],"noise":[137],"during":[138],"texture":[139,151],"learning.":[140],"The":[141],"proposed":[142],"can":[144],"perform":[145],"learn":[149],"videos.":[154],"validate":[156],"our":[157,193],"sequences":[160],"extracted":[161],"EndoVis17/18,":[163],"SAR-RARP,":[164],"an":[166],"in-house":[167],"dataset,":[168],"showing":[169],"superior":[170],"photometric":[171],"quality":[172],"improved":[174],"geometric":[175],"accuracy":[176],"over":[177],"state-of-the-art":[178],"baselines.":[179],"further":[181],"demonstrate":[182],"downstream":[184],"keypoint":[185],"detection":[186],"task":[187],"unseen-pose":[189],"augmentation":[191],"instrument":[195],"improves":[197],"performance.":[198]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-26T00:00:00"}