{"id":"W3009261761","doi":"10.1109/iccv.2019.00799","title":"Agile Depth Sensing Using Triangulation Light Curtains","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Parks and Wilderness Society","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Triangulation; Shutter; Lidar; Sample (material); Ranging; Sampling (signal processing); Computer graphics (images); Geography; Remote sensing; Optics","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":[0.000278918,0.0006649325,0.0005522544,0.0006592473,0.0003103758,0.0006790425,0.001060771,0.0005391804,0.002444817],"category_scores_gemma":[0.00109102,0.0005844535,0.0004685551,0.0004998375,0.0004579519,0.001071099,0.001958732,0.0007045013,0.0006783397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004911459,"about_ca_system_score_gemma":0.000516304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002288012,"about_ca_topic_score_gemma":0.002913659,"domain_scores_codex":[0.9991472,0.00009094482,0.00002302684,0.000178212,0.000469798,0.00009092454],"domain_scores_gemma":[0.9993894,0.0001751962,0.000104442,0.00010654,0.0001624002,0.00006200356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002913548,0.00007741337,0.001303547,0.0002247241,0.00004651303,0.0001940912,0.0004742567,0.01056598,0.7977027,0.003509692,0.002646313,0.1829634],"study_design_scores_gemma":[0.0002291751,0.001340284,0.007349815,0.0001088782,0.00008763175,0.002427448,0.0003523791,0.3913413,0.5444144,0.004169157,0.04781979,0.0003597758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04192399,0.000469195,0.9509623,0.0001127764,0.00009075009,0.0001598177,0.0001144247,0.002535014,0.003631732],"genre_scores_gemma":[0.4090703,0.0005441196,0.5841376,0.000240339,0.00005893441,0.0002357905,0.000166404,0.0002925072,0.005253989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002444817,"threshold_uncertainty_score":0.008178711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239298458077326,"score_gpt":0.2127042577837393,"score_spread":0.200311273202966,"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."}}