{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004999,0.0009909497,0.001129871,0.001663048,0.0004878697,0.001687811,0.001217475,0.000974806,0.00249982],"category_scores_gemma":[0.004384093,0.001100276,0.001055443,0.001061305,0.0008574578,0.001744459,0.001957545,0.001339384,0.001237867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007533984,"about_ca_system_score_gemma":0.0009857044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005464227,"about_ca_topic_score_gemma":0.005516856,"domain_scores_codex":[0.9988488,0.0001628378,0.00004572914,0.0001870462,0.00066355,0.00009200771],"domain_scores_gemma":[0.9985721,0.0005044912,0.0001557266,0.000418386,0.0002917833,0.00005747686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002140992,0.00005075426,0.00203722,0.0002521326,0.0001026441,0.000353517,0.0005719629,0.6036895,0.06517608,0.01611758,0.003492797,0.3079418],"study_design_scores_gemma":[0.00002114626,0.00004048041,0.0003542348,0.00001950695,0.00001039273,0.0001949582,0.00009428724,0.9722943,0.01493855,0.007795987,0.004204155,0.0000321128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004478265,0.00004334615,0.9945956,0.00002092107,0.000007897949,0.00001666554,0.0000261455,0.0003844305,0.0004267024],"genre_scores_gemma":[0.1263733,0.0001701022,0.8715374,0.0000308625,0.00001279176,0.00005113028,0.0002841275,0.0003360228,0.001204114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005464227,"threshold_uncertainty_score":0.01086485,"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."}}