{"id":"W2061941037","doi":"10.1145/2816795.2818088","title":"Deformation-driven topology-varying 3D shape correspondence","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Topology (electrical circuits); Deformation (meteorology); Piecewise; Distortion (music); Shape analysis (program analysis); Mathematics; Geometry; Geometric shape; Topological skeleton; Computer science; Active shape model; Artificial intelligence; Mathematical analysis; Physics; Segmentation; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000738425,0.0008904745,0.001082589,0.001147027,0.0007753293,0.001287824,0.002651172,0.001831988,0.003283005],"category_scores_gemma":[0.002567251,0.0007940204,0.001277514,0.001294599,0.001498665,0.001828559,0.002939374,0.001616274,0.001046623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008694038,"about_ca_system_score_gemma":0.0009370431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100438,"about_ca_topic_score_gemma":0.001912702,"domain_scores_codex":[0.9990208,0.0001634261,0.0000366189,0.0002115643,0.0004983479,0.00006922416],"domain_scores_gemma":[0.9990956,0.0002445755,0.00009817802,0.0004003559,0.0001072939,0.00005408323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001142098,0.000102449,0.0007428075,0.0001060062,0.00006436145,0.0002350294,0.000243878,0.8287459,0.03807654,0.0333295,0.002084095,0.0961552],"study_design_scores_gemma":[0.00001038406,0.00003927922,0.0001256168,0.000005777352,0.000006702811,0.0001298318,0.00002883359,0.9796529,0.00747542,0.01063919,0.001866671,0.00001933683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02852114,0.0001208286,0.9669327,0.0001117575,0.00003694721,0.00006785162,0.00009051753,0.001194071,0.002924164],"genre_scores_gemma":[0.5539686,0.0001472245,0.4408357,0.0001721781,0.00002756397,0.0001779522,0.0003521582,0.0006632149,0.00365552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003283005,"threshold_uncertainty_score":0.01098269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03564344140962938,"score_gpt":0.252920786400792,"score_spread":0.2172773449911626,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}