{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000540191,0.001357071,0.0007692963,0.0009396233,0.0003690469,0.0009133297,0.002761948,0.001436888,0.007470957],"category_scores_gemma":[0.001111317,0.0005185753,0.0008276133,0.001140264,0.000715451,0.001845191,0.001539112,0.001707545,0.002565961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195771,"about_ca_system_score_gemma":0.001408853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01809804,"about_ca_topic_score_gemma":0.02695216,"domain_scores_codex":[0.9997316,0.00003315492,0.00001332066,0.0000946254,0.00009118088,0.00003605715],"domain_scores_gemma":[0.9997188,0.00005726608,0.00002063081,0.00007328886,0.0001009329,0.00002901539],"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":[0.000181666,0.0001610944,0.001239709,0.000118166,0.0001615202,0.0001339959,0.000042674,0.5454582,0.01087334,0.01680359,0.02137065,0.4034554],"study_design_scores_gemma":[0.000007740428,0.00002624581,0.00009163484,0.000003953221,0.000005449272,0.00001575392,0.000004007639,0.9920959,0.001969937,0.004056687,0.001716787,0.00000591628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02656806,0.0007588649,0.9587945,0.0003700358,0.0001538897,0.0001049469,0.001033092,0.007884221,0.004332426],"genre_scores_gemma":[0.4433686,0.0009607238,0.5221316,0.0006064441,0.0001204197,0.0003763066,0.008271116,0.0005944779,0.02357031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01809804,"threshold_uncertainty_score":0.03598535,"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."}}