{"id":"W4379876687","doi":"10.1109/plans53410.2023.10139984","title":"Accurate and Scalable Contour-based Camera Pose Estimation Using Deep Learning with Synthetic Data","year":2023,"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 Calgary","funders":"","keywords":"Pose; Artificial intelligence; Robustness (evolution); Computer science; Computer vision; 3D pose estimation; Scalability; Synthetic data; Pattern recognition (psychology); Object detection; Object (grammar); Training set; Deep learning; Cognitive neuroscience of visual object recognition","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":[0.0001372853,0.0001058686,0.0001107557,0.00009321589,0.0001011754,0.0001113834,0.00007358281,0.00004092419,0.00003752202],"category_scores_gemma":[0.00006040174,0.00009266873,0.000007352633,0.0002681306,0.000023827,0.0002221375,0.00002451622,0.00007937808,0.00002012344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000241409,"about_ca_system_score_gemma":0.00001475435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007300015,"about_ca_topic_score_gemma":0.00003443235,"domain_scores_codex":[0.9993886,0.00002270645,0.0001248788,0.0001760386,0.0001118399,0.0001759334],"domain_scores_gemma":[0.9995771,0.00007896693,0.00002308361,0.0002295148,0.00003694671,0.00005437023],"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":[0.000005208962,0.00000357496,0.0003578101,0.00004746297,0.00001146755,0.000006734248,0.0000332965,0.9947963,0.001513641,0.00006132471,0.00006370039,0.003099514],"study_design_scores_gemma":[0.0002616919,0.00002265721,0.0003757773,0.00005354056,0.00002659979,0.000004293804,0.00007884942,0.9980391,0.0008924858,0.00001437721,0.00009375575,0.0001368444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2170372,0.00004514702,0.7819107,0.00005140902,0.00005328621,0.0001044593,0.000003910342,0.0004115433,0.0003823477],"genre_scores_gemma":[0.9851006,0.00001972927,0.01456171,0.0000246245,0.00001809946,0.000001867996,0.0001771284,0.00003523176,0.00006101567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7680634,"threshold_uncertainty_score":0.3778921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131029973090095,"score_gpt":0.2505544453717743,"score_spread":0.2192441456408734,"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."}}