{"id":"W2962748714","doi":"10.1111/cgf.13804","title":"SiamesePointNet: A Siamese Point Network Architecture for Learning 3D Shape Descriptor","year":2019,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Shape context; Artificial intelligence; Transformation (genetics); Computer science; Pattern recognition (psychology); Constraint (computer-aided design); Matching (statistics); Feature (linguistics); Context (archaeology); Shape analysis (program analysis); Benchmark (surveying); Heat kernel signature; Point (geometry); Geometric transformation; Mathematics; Computer vision; Active shape model; Image (mathematics); Geometry; Segmentation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002429186,0.0003047026,0.0003928156,0.0002213758,0.000151806,0.0001129727,0.0002979198,0.0001591173,0.0000517921],"category_scores_gemma":[0.00001026997,0.0002930269,0.000380481,0.0004189913,0.00003078764,0.00009654408,0.00009053082,0.0004943629,0.00005050861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002768272,"about_ca_system_score_gemma":0.00001437007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005332898,"about_ca_topic_score_gemma":0.00001719097,"domain_scores_codex":[0.9983869,0.00003310273,0.0003199552,0.0003736685,0.0001874638,0.000698895],"domain_scores_gemma":[0.9992689,0.0001114983,0.00005563972,0.0003579226,0.00007346932,0.0001325448],"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":[0.00001348193,0.00001528056,0.002767068,0.0001096996,0.0002003093,0.000002980478,0.000227269,0.965804,0.00008758304,0.001347275,0.007609094,0.02181599],"study_design_scores_gemma":[0.0004024895,0.0001304577,0.0001102926,0.00009692048,0.00004173063,0.000008054922,0.00002600134,0.971104,0.00001976832,0.003641448,0.02406182,0.0003569998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05725707,0.0008261111,0.9394875,0.000298631,0.0008376781,0.0003109536,0.0000072701,0.0006384053,0.0003363521],"genre_scores_gemma":[0.9617473,0.00005716135,0.0364964,0.0006875346,0.0005960315,0.00003590827,0.0000610026,0.0001169728,0.000201641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9044903,"threshold_uncertainty_score":0.9999522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007019028644589117,"score_gpt":0.1925548960615228,"score_spread":0.1855358674169337,"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."}}