{"id":"W3083345301","doi":"10.3390/s20185098","title":"Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Pose; Artificial intelligence; Computer vision; Computer science; Object (grammar); 3D pose estimation; Point cloud; RGB color model; Histogram; Cognitive neuroscience of visual object recognition; Feature (linguistics); Pattern recognition (psychology); Point (geometry); 3D single-object recognition; Matching (statistics); Mathematics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009429387,0.0001929698,0.0001991625,0.00006992122,0.00009232873,0.00008230897,0.00006114716,0.0001390986,0.000006961484],"category_scores_gemma":[0.00007226085,0.0002032546,0.00004375725,0.0001758089,0.00002633159,0.0001089871,0.000009711813,0.000105944,0.000009953198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000334034,"about_ca_system_score_gemma":0.00001122566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001422891,"about_ca_topic_score_gemma":0.000009409516,"domain_scores_codex":[0.9990972,0.00004687885,0.0002217965,0.0002843544,0.0001271594,0.0002226185],"domain_scores_gemma":[0.9995453,0.00005892448,0.00004090303,0.0001381465,0.00007415263,0.0001425882],"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":[0.00005939764,0.00002923686,0.00004730936,0.000329754,0.00003375733,0.000007293113,0.001031911,0.9472098,0.002105735,0.00004133991,0.0001386982,0.0489658],"study_design_scores_gemma":[0.0004671159,0.0001088145,0.0001728797,0.00002180975,0.00004845937,0.00002360475,0.0004334602,0.995148,0.003000935,0.0001556607,0.0001439914,0.000275267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7744237,0.0001481317,0.2230631,0.00005494475,0.0001801117,0.0005994546,0.0001002194,0.0003839064,0.001046374],"genre_scores_gemma":[0.9677653,0.00005453922,0.0311027,0.0001367424,0.0002014972,0.00002279046,0.0006276986,0.00007131883,0.00001743074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1933415,"threshold_uncertainty_score":0.8288481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03064938341109053,"score_gpt":0.2145414210289737,"score_spread":0.1838920376178832,"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."}}