{"id":"W2609795132","doi":"10.1109/msp.2017.2669347","title":"Computational Depth Sensing : Toward high-performance commodity depth cameras","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Computer science; Focus (optics); Light field; Commodity; Artificial intelligence; Field (mathematics); Computer vision; Depth of field; Data science; Computer graphics (images); Optics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001319063,0.0002846143,0.0003283636,0.00006182745,0.001026437,0.0003575328,0.0004191065,0.00007767556,0.00003026174],"category_scores_gemma":[0.00002462232,0.0002662968,0.00005989905,0.0001022703,0.0005114903,0.0005914825,0.0001499848,0.0004328743,0.0001217012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005351876,"about_ca_system_score_gemma":0.0001249019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005974238,"about_ca_topic_score_gemma":0.00001149173,"domain_scores_codex":[0.9985021,0.0000225695,0.0003003583,0.0004059322,0.0003075991,0.0004614278],"domain_scores_gemma":[0.9988696,0.00007576658,0.0003425783,0.0003866836,0.0002306875,0.00009466647],"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":[0.00004778661,0.0000897491,0.01384502,0.00005598237,0.00004175959,0.00001518274,0.0000668385,0.02759863,0.003391277,0.001348504,0.0005280796,0.9529712],"study_design_scores_gemma":[0.002286061,0.0002306683,0.1349441,0.0005899594,0.000122621,0.00003134899,0.00009981536,0.6892134,0.06083015,0.1082054,0.001916592,0.001529911],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5638199,0.00002532324,0.4280599,0.0006360447,0.0001634397,0.0001250135,0.00001423224,0.0002749295,0.006881205],"genre_scores_gemma":[0.9266089,0.000001058292,0.07274383,0.00007496068,0.0003497175,0.000002325693,0.00002918773,0.00003612685,0.0001538592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9514413,"threshold_uncertainty_score":0.9999789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02787184519403606,"score_gpt":0.2814110853398668,"score_spread":0.2535392401458307,"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."}}