{"id":"W2515283259","doi":"10.1109/icma.2016.7558806","title":"Efficient monocular coarse-to-fine object pose estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"RANSAC; Artificial intelligence; Pose; Matching (statistics); Computer science; Object (grammar); Computer vision; Feature (linguistics); Image (mathematics); Pattern recognition (psychology); 3D pose estimation; Artificial neural network; Cluster analysis; Mathematics","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":[0.0005914512,0.001400796,0.001770191,0.001332005,0.0005178595,0.00105886,0.001762286,0.001010999,0.005228199],"category_scores_gemma":[0.001410915,0.0008397859,0.0008600779,0.001615223,0.0003762521,0.001774396,0.002285438,0.0007998942,0.002958412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007096201,"about_ca_system_score_gemma":0.00141239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009407051,"about_ca_topic_score_gemma":0.01798486,"domain_scores_codex":[0.9989737,0.00007812474,0.00003703513,0.0003003265,0.0004637989,0.0001469058],"domain_scores_gemma":[0.9994924,0.00007397114,0.00006203219,0.0001974824,0.0001411801,0.00003283198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001826193,0.0001037982,0.00122283,0.0001331533,0.00007106777,0.00009848301,0.0000781542,0.06354349,0.06638791,0.002303649,0.005847886,0.8600269],"study_design_scores_gemma":[0.00001998832,0.0001005977,0.004599744,0.00001597337,0.00002678414,0.0002965881,0.0000696307,0.9474438,0.03815771,0.004599486,0.004629975,0.00003974796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00902662,0.000259958,0.9870681,0.00004289294,0.00001785871,0.00004273761,0.0001773671,0.002213327,0.001151178],"genre_scores_gemma":[0.2690363,0.0005183506,0.7228885,0.0001325768,0.00004786616,0.0001208492,0.001325668,0.0003251173,0.005604813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009407051,"threshold_uncertainty_score":0.01870459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007006350590819689,"score_gpt":0.2016214580798091,"score_spread":0.1946151074889894,"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."}}