{"id":"W3150360606","doi":"10.1109/lra.2021.3068669","title":"Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Specular reflection; Artificial intelligence; Polarimetry; Computer vision; Azimuth; Computer science; Polarization (electrochemistry); Monocular; Remote sensing; Optics; Geology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003117803,0.0008637502,0.0006133573,0.0005412989,0.0002242249,0.0007779377,0.0006815653,0.0006034051,0.001780169],"category_scores_gemma":[0.001357969,0.0004970751,0.0004501866,0.0007076521,0.0005104085,0.001524489,0.001667807,0.001254997,0.0008072936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002704691,"about_ca_system_score_gemma":0.0008314367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001999299,"about_ca_topic_score_gemma":0.002783321,"domain_scores_codex":[0.9997343,0.00005616377,0.000008924573,0.00005270159,0.0001143874,0.00003338129],"domain_scores_gemma":[0.9996279,0.0001012387,0.00005055167,0.0001107766,0.00008365659,0.00002586356],"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":[0.0003705736,0.0001326312,0.001077484,0.0002875615,0.00007222766,0.0001734346,0.0002625925,0.2799364,0.09684745,0.01923985,0.005132764,0.5964671],"study_design_scores_gemma":[0.00002138051,0.00006707056,0.0004069353,0.00002172323,0.00001402851,0.0002246112,0.00004623557,0.96015,0.02714291,0.008231772,0.003645823,0.00002746807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008485816,0.000106834,0.9897613,0.00008708892,0.00001968582,0.00001885866,0.00007858095,0.0003657197,0.001076164],"genre_scores_gemma":[0.2369334,0.0004716715,0.7587743,0.0002026789,0.00005775742,0.00008496976,0.0005849838,0.0001359676,0.002754369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001999299,"threshold_uncertainty_score":0.005955279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811566135327421,"score_gpt":0.2184393501550099,"score_spread":0.2003236888017357,"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."}}