{"id":"W4200200310","doi":"10.1109/iros51168.2021.9635875","title":"PLUMENet: Efficient 3D Object Detection from Stereo Images","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Point cloud; Computer vision; Lidar; Benchmark (surveying); Metric (unit); Object detection; Object (grammar); Feature (linguistics); Representation (politics); Exploit; Inference; Detector; Key (lock); Pattern recognition (psychology); Remote sensing; Engineering; Geography","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":[0.0001939953,0.0003207529,0.000332356,0.0001561631,0.0002373457,0.0007627687,0.0007493994,0.0001168842,0.000237733],"category_scores_gemma":[0.00004980163,0.0003007826,0.000105537,0.0003184498,0.00007838695,0.0002638974,0.000272026,0.0003106976,0.0002560285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001796967,"about_ca_system_score_gemma":0.00009832333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002195949,"about_ca_topic_score_gemma":0.0000914269,"domain_scores_codex":[0.997222,0.0001629896,0.0006031602,0.001005501,0.0006678833,0.0003384634],"domain_scores_gemma":[0.9981247,0.0002163882,0.0002824621,0.0006874923,0.0005044857,0.0001844286],"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":[0.0001120826,0.0007959015,0.0008173661,0.00007842668,0.0005068165,0.0002709251,0.001646589,0.346364,0.1704532,0.2260056,0.001693244,0.2512559],"study_design_scores_gemma":[0.0003880963,0.0001173499,0.0008634134,0.0003031751,0.00002100239,0.00007936715,0.0003431959,0.9283206,0.06046772,0.002265463,0.006290151,0.0005403981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02712689,0.0005831186,0.961106,0.0008337137,0.003934375,0.0004028662,0.00007233308,0.0001219574,0.005818758],"genre_scores_gemma":[0.9928942,0.0006304196,0.002748554,0.000217101,0.0004889751,0.0001443034,0.00005190534,0.00002209198,0.002802404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9657674,"threshold_uncertainty_score":0.9999444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04345949719033659,"score_gpt":0.2905566820600707,"score_spread":0.2470971848697341,"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."}}