{"id":"W2160209983","doi":"10.1109/ccece.2006.277433","title":"Vertex-Based Anisotropic Smoothing of 3D Mesh Data","year":2006,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smoothing; Laplacian smoothing; Vertex (graph theory); Computer science; Noise reduction; Polygon mesh; Triangle mesh; Algorithm; Mesh generation; Construct (python library); Anisotropic diffusion; Partial differential equation; Mathematical optimization; Mathematics; Theoretical computer science; Artificial intelligence; Finite element method; Mathematical analysis; Computer vision; Image (mathematics); Engineering","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":[0.0008692667,0.0005405868,0.0008609466,0.001304404,0.0004147641,0.001376922,0.001514867,0.001634459,0.0007105532],"category_scores_gemma":[0.002701298,0.0005069973,0.00115174,0.001130335,0.0008251825,0.001232869,0.001172147,0.001271786,0.0004768503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004247698,"about_ca_system_score_gemma":0.0006951066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001814323,"about_ca_topic_score_gemma":0.001982782,"domain_scores_codex":[0.9993353,0.0001039252,0.00004129466,0.0001017458,0.0003744388,0.0000432431],"domain_scores_gemma":[0.999071,0.000283579,0.0001063534,0.0002528193,0.0002389063,0.00004726852],"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":[0.0001891706,0.00007817646,0.002008693,0.0003359023,0.0001489173,0.0003736597,0.0004438981,0.3506486,0.2519581,0.04857442,0.002809402,0.3424311],"study_design_scores_gemma":[0.000007981152,0.00002534468,0.000203502,0.000008071251,0.00001453018,0.000144705,0.00001898055,0.9566836,0.03313797,0.006425246,0.003302532,0.00002753943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004377253,0.00005561941,0.9950984,0.0000553591,0.00002330524,0.00001107146,0.00001635421,0.0001410275,0.0002215838],"genre_scores_gemma":[0.1282319,0.0003857398,0.8695635,0.00009509124,0.00005412997,0.00005973662,0.0001726454,0.0001488399,0.001288502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001814323,"threshold_uncertainty_score":0.004597187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790253988436143,"score_gpt":0.2179277275019924,"score_spread":0.200025187617631,"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."}}