{"id":"W2162573538","doi":"10.1109/iros.2011.6095027","title":"Mobile 3D object detection in clutter","year":2011,"lang":"en","type":"article","venue":"2011 IEEE/RSJ International Conference on Intelligent Robots and Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Clutter; Object detection; Cognitive neuroscience of visual object recognition; Object (grammar); Point cloud; Minimum bounding box; Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Radar","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":[0.00063523,0.0009698994,0.001511877,0.001747291,0.000605524,0.00125903,0.001756817,0.001119442,0.001397712],"category_scores_gemma":[0.001795841,0.0008772889,0.001132845,0.001252011,0.0009505733,0.001485543,0.00223359,0.0008944286,0.001684907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005969463,"about_ca_system_score_gemma":0.0005627563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003003639,"about_ca_topic_score_gemma":0.004178181,"domain_scores_codex":[0.9988702,0.0001227483,0.00002683366,0.0003984327,0.0004530346,0.0001287054],"domain_scores_gemma":[0.9991577,0.0002821471,0.0001002101,0.0002474663,0.0001431041,0.0000695198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004416548,0.0001276516,0.004449519,0.0002036176,0.0002086079,0.0006997273,0.0005346151,0.1268569,0.1211848,0.008572973,0.0057278,0.730992],"study_design_scores_gemma":[0.00002263752,0.0000995294,0.002534455,0.00001762822,0.00003561154,0.001060387,0.00007597265,0.9433894,0.03687102,0.009260423,0.006579013,0.00005384792],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01150947,0.0002065558,0.985415,0.00003906439,0.0000257337,0.00001833184,0.00007259953,0.002181054,0.0005321649],"genre_scores_gemma":[0.2705364,0.0002771185,0.7260949,0.0001926766,0.00006269335,0.00006750832,0.0005779425,0.0003730581,0.001817747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003003639,"threshold_uncertainty_score":0.005972266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05853965936257504,"score_gpt":0.253059813786128,"score_spread":0.194520154423553,"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."}}