{"id":"W2806001719","doi":"10.1109/3dv.2017.00036","title":"Generalized Pose Estimation from Line Correspondences with Known Vertical Direction","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institutul de Fizică Atomică; European Regional Development Fund; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Agence Universitaire de la Francophonie","keywords":"Projection (relational algebra); Solver; Projection plane; Computer vision; Line (geometry); Artificial intelligence; Computer science; Pose; Orientation (vector space); Plane (geometry); Line segment; Least-squares function approximation; Image plane; Algorithm; Mathematics; Image (mathematics); Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003670797,0.001525847,0.001470494,0.001490763,0.0004206155,0.0009584891,0.001432764,0.0008929137,0.00290917],"category_scores_gemma":[0.001132287,0.0007667438,0.0009427147,0.001696697,0.0005643785,0.001282423,0.001776428,0.001070431,0.001919934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003199769,"about_ca_system_score_gemma":0.0008810142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002746416,"about_ca_topic_score_gemma":0.003753885,"domain_scores_codex":[0.9992493,0.0001312664,0.00002868001,0.0002646158,0.0002551443,0.00007098974],"domain_scores_gemma":[0.9995905,0.00005993982,0.00009453349,0.0001054585,0.0001239902,0.00002565271],"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":[0.0001991494,0.00008203247,0.001520442,0.0002235986,0.0001340619,0.0002459201,0.0002010322,0.2359898,0.04482613,0.008688903,0.005617323,0.7022716],"study_design_scores_gemma":[0.00003866536,0.0001355685,0.001063825,0.00002115609,0.00002329764,0.0002251682,0.0001047455,0.9676999,0.01643256,0.007956414,0.006258137,0.0000405272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007767725,0.00007258412,0.990661,0.00001858121,0.00001961717,0.00001688844,0.00005826788,0.001014346,0.0003711024],"genre_scores_gemma":[0.2491786,0.0002519731,0.7457897,0.00006624718,0.00007424202,0.0001505528,0.00115958,0.0003063398,0.003022818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00290917,"threshold_uncertainty_score":0.009732187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01495631981701667,"score_gpt":0.2404216349403551,"score_spread":0.2254653151233384,"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."}}