{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001797628,0.0002063384,0.0002079978,0.000279052,0.00004111869,0.00009135083,0.0001665955,0.0001334361,0.0004102229],"category_scores_gemma":[0.00000739013,0.0001891777,0.00004486651,0.00007064173,0.00003647428,0.0001323432,0.00001577382,0.0001902553,0.0002097144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001105444,"about_ca_system_score_gemma":0.0000140872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004879104,"about_ca_topic_score_gemma":0.0002010458,"domain_scores_codex":[0.9987994,0.00004607079,0.0004308627,0.0002629819,0.0002457973,0.0002148854],"domain_scores_gemma":[0.9995292,0.00002279737,0.00006645612,0.0001688733,0.0001284751,0.00008416698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002357316,0.0004071837,0.007057863,0.0002904193,0.0003200101,0.00008132096,0.006065059,0.8678429,0.01574633,0.04003466,0.001668488,0.06025007],"study_design_scores_gemma":[0.0002465186,0.0002095665,0.0009282283,0.0001705062,0.000009229988,0.00001814351,0.0004624663,0.9874662,0.008642963,0.0002657761,0.001280332,0.0003000739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3212495,0.0005593779,0.5717793,0.00003111332,0.01105515,0.0012036,0.00003669798,0.0003516076,0.09373362],"genre_scores_gemma":[0.9982147,0.0005201263,0.0001465302,0.000036761,0.0001473207,0.00008079827,0.0000158969,0.00002819678,0.0008096332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6769652,"threshold_uncertainty_score":0.7714441,"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."}}