{"id":"W2099915003","doi":"10.1109/isvd.2009.13","title":"High Quality Visual Hull Reconstruction by Delaunay Refinement","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Delaunay triangulation; Bowyer–Watson algorithm; Surface triangulation; Voronoi diagram; Visual hull; Constrained Delaunay triangulation; Computer vision; Computer science; Artificial intelligence; Triangulation; Mesh generation; Algorithm; Mathematics; Iterative reconstruction; Geometry; Finite element method; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001892092,0.00008474285,0.00009912776,0.00004054137,0.00009286019,0.00007594212,0.0002308214,0.00002342821,0.0001216568],"category_scores_gemma":[0.00002138528,0.00007170494,0.00003228548,0.0001684684,0.00001601196,0.0005322765,0.00005232753,0.00007512952,0.00006792724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003790799,"about_ca_system_score_gemma":0.00001403767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003358991,"about_ca_topic_score_gemma":0.000002275568,"domain_scores_codex":[0.9990971,0.00003968321,0.0002259303,0.0002836675,0.0001789354,0.0001746485],"domain_scores_gemma":[0.9995383,0.00002149153,0.00006554306,0.0002550008,0.00004705515,0.00007262614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002675891,0.0000411311,0.00006291046,7.895569e-7,0.000001589909,6.495029e-7,0.00002313341,0.0000067175,0.01187154,0.03911142,0.00477411,0.9441033],"study_design_scores_gemma":[0.004757496,0.001561394,0.03151318,0.0001109639,0.00001480745,0.0001685514,0.0004224184,0.2100972,0.4071951,0.1672394,0.1745505,0.002369037],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01657982,0.00003811281,0.9724007,0.003669419,0.0003090856,0.00005272915,5.579402e-7,0.0002365874,0.006712952],"genre_scores_gemma":[0.5733114,0.0000192668,0.4209113,0.003940482,0.00004334317,0.000001910388,0.000003386685,0.000003139559,0.001765814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9417343,"threshold_uncertainty_score":0.2924043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258164759669307,"score_gpt":0.3209043753937285,"score_spread":0.3083227277970355,"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."}}