{"id":"W4396753389","doi":"10.1109/tim.2024.3398108","title":"Robust Object Pose Tracking for Augmented Reality Guidance and Teleoperation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Augmented reality; Teleoperation; Pose; Computer vision; Computer science; Artificial intelligence; Object (grammar); Video tracking; Tracking (education); Virtual reality; Telerobotics; Mobile robot; Robot; Psychology","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":[0.0006070264,0.0001521005,0.000113559,0.000151163,0.0003969754,0.000353721,0.0001097533,0.00005450236,0.00000831307],"category_scores_gemma":[0.000006629286,0.0001489627,0.00004933267,0.0002614901,0.00004573642,0.000580643,0.000002214622,0.000113599,0.000004708698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002301401,"about_ca_system_score_gemma":0.00008860645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005634934,"about_ca_topic_score_gemma":0.0001620004,"domain_scores_codex":[0.9986179,0.00006473135,0.0003071108,0.00046736,0.0003715813,0.0001712668],"domain_scores_gemma":[0.9994397,0.00005089224,0.0000508563,0.0002233033,0.0001416356,0.00009359376],"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":[0.00005578221,0.0002794359,0.00001496392,0.0002783407,0.0001807733,0.000001463781,0.002352143,0.008975124,0.03788158,0.0198966,0.0006760429,0.9294077],"study_design_scores_gemma":[0.002872526,0.000652889,0.00221053,0.0005499936,0.0002196584,0.00006481642,0.0008344284,0.7227589,0.25756,0.003079711,0.00844427,0.0007522896],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004645425,0.0001560419,0.990166,0.003172894,0.0005249939,0.0008427799,0.00003621956,0.0002322215,0.0002234017],"genre_scores_gemma":[0.9881343,0.0001415096,0.01085589,0.0002782649,0.00003368354,0.0004438162,0.000008667556,0.0000122877,0.00009162685],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9834888,"threshold_uncertainty_score":0.6074523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0948334166498744,"score_gpt":0.2981901215377086,"score_spread":0.2033567048878342,"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."}}