{"id":"W2979545157","doi":"10.1109/mmar.2019.8864723","title":"Achievable Stereo Vision Depth Accuracy with Changing Camera Baseline","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Pixel; Computer vision; Stereo camera; Baseline (sea); Computer science; Stereopsis; Computer stereo vision; Gaussian; Depth map; Image (mathematics); Geology; Physics","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":[0.001871384,0.0004822837,0.0004044844,0.0006532933,0.0003858082,0.0009155436,0.0007494846,0.0009854255,0.001820419],"category_scores_gemma":[0.01526567,0.0003714463,0.0003135352,0.0007159496,0.0005660664,0.001529756,0.001537275,0.0008179718,0.0003023553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009494102,"about_ca_system_score_gemma":0.0005181182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00156152,"about_ca_topic_score_gemma":0.001302629,"domain_scores_codex":[0.9960736,0.0004625535,0.000194938,0.0005963008,0.002140927,0.0005316373],"domain_scores_gemma":[0.9912315,0.004424619,0.001086085,0.0009986623,0.002012693,0.0002464285],"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":[0.002404125,0.0001904268,0.01834948,0.0007494313,0.0000911391,0.0008558056,0.0007190497,0.08698795,0.7456854,0.003990532,0.0007759992,0.1392007],"study_design_scores_gemma":[0.0001547848,0.002768606,0.05571734,0.0001286641,0.0001198217,0.001900131,0.0004740244,0.1120528,0.8196386,0.003457754,0.003414182,0.0001733927],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8102905,0.001103156,0.1805117,0.0002888625,0.00009078856,0.00005385655,0.0005438675,0.0009704081,0.00614687],"genre_scores_gemma":[0.9806845,0.0001695576,0.01845713,0.00004383408,0.0000114659,0.00001160936,0.0001603653,0.00006350611,0.0003981019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001871384,"threshold_uncertainty_score":0.009896934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006189426347727004,"score_gpt":0.2106610017413051,"score_spread":0.2044715753935781,"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."}}