{"id":"W3129575853","doi":"10.1109/vtc2020-fall49728.2020.9348762","title":"Linear-PoseNet: A Real-Time Camera Pose Estimation System Using Linear Regression and Principal Component Analysis","year":2020,"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 Victoria","funders":"Science and Engineering Research Council","keywords":"Upsampling; Computer science; Artificial intelligence; Principal component analysis; Computer vision; Orientation (vector space); Position (finance); Component (thermodynamics); Pattern recognition (psychology); Artificial neural network; Linear regression; Image (mathematics); Machine learning; 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":[0.0006043749,0.00188794,0.0007897549,0.0007709806,0.0003319818,0.0008810167,0.002249117,0.001119069,0.01327455],"category_scores_gemma":[0.001459986,0.0008925641,0.0005976742,0.0006845845,0.0004229743,0.001930426,0.001272854,0.001498213,0.00704856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000669243,"about_ca_system_score_gemma":0.000965342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007443625,"about_ca_topic_score_gemma":0.01282878,"domain_scores_codex":[0.9995906,0.00005203681,0.0000166338,0.000194357,0.0001059887,0.00004037485],"domain_scores_gemma":[0.9996723,0.00005727515,0.00003876388,0.00009374227,0.0001126997,0.00002529141],"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":[0.0005224097,0.0002993153,0.001870548,0.0003400717,0.0003071893,0.0003088187,0.0001386523,0.09781536,0.05019335,0.004277661,0.05432002,0.7896066],"study_design_scores_gemma":[0.00006094004,0.0001470395,0.001425217,0.00002928085,0.00004025076,0.0001743284,0.00003474853,0.9507448,0.03019514,0.002886462,0.01420992,0.00005182999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007736543,0.0002072503,0.9246058,0.0001135477,0.0001442961,0.00009517449,0.0006962108,0.06417105,0.002230214],"genre_scores_gemma":[0.2136597,0.0003156308,0.7650933,0.0004179329,0.0001005747,0.0003809471,0.005165014,0.003050006,0.01181692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01327455,"threshold_uncertainty_score":0.04440778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893805934781144,"score_gpt":0.2411371536968173,"score_spread":0.2221990943490058,"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."}}