{"id":"W4383108328","doi":"10.1109/icra48891.2023.10160529","title":"6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pose; Artificial intelligence; Computer science; Computer vision; Rotation (mathematics); Object (grammar); RGB color model; Translation (biology); Orientation (vector space); 3D pose estimation; Process (computing); Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007905708,0.0001217022,0.0001210965,0.0001375764,0.00008085227,0.00005698438,0.00004802048,0.00009802602,0.00002131159],"category_scores_gemma":[0.00006872389,0.0001166531,0.00004062318,0.0003128264,0.0000074815,0.00009661534,0.000006857687,0.00005266281,0.00002807595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005424495,"about_ca_system_score_gemma":0.00001078086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000925783,"about_ca_topic_score_gemma":0.000006080244,"domain_scores_codex":[0.9994012,0.00001215691,0.0001734846,0.0001366974,0.0001044963,0.0001720151],"domain_scores_gemma":[0.9996818,0.00006831257,0.00002467114,0.0001255142,0.00005953597,0.00004022018],"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":[0.000003437385,0.00001283846,0.000008167437,0.0001005562,0.00001021408,0.000001043549,0.000100439,0.9944736,0.0005865788,0.0006468268,0.0003187279,0.003737557],"study_design_scores_gemma":[0.0003031924,0.00002326391,0.00007161284,0.00006477554,0.00001467989,8.28398e-7,0.00005331142,0.9965188,0.002642791,0.00007512429,0.00008544403,0.0001461964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01077118,0.00003490387,0.9876823,0.00004688334,0.0003487734,0.0003468407,0.000007301953,0.0005671588,0.0001946328],"genre_scores_gemma":[0.7646272,0.00006734914,0.2345662,0.000112836,0.0000826222,0.00002095797,0.0002869714,0.00006849763,0.0001673448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7538561,"threshold_uncertainty_score":0.4756974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03827738884671936,"score_gpt":0.2821304716081396,"score_spread":0.2438530827614203,"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."}}