{"id":"W4226344270","doi":"10.1109/cvpr52688.2022.00118","title":"RBGNet: Ray-based Grouping for 3D Object Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Point cloud; Object detection; Artificial intelligence; Object (grammar); Feature (linguistics); Computer vision; Point (geometry); Process (computing); RGB color model; Pattern recognition (psychology); Sample (material); Cluster (spacecraft); Detector; Code (set theory); Mathematics","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":[0.0003626668,0.0003252015,0.0003134782,0.0003030197,0.0009954295,0.0003136162,0.0005797106,0.00007742882,0.000218703],"category_scores_gemma":[0.000009542167,0.0003403059,0.0001336007,0.0005109679,0.00005087537,0.0003905279,0.0002761081,0.0004313641,0.00006401443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009367654,"about_ca_system_score_gemma":0.0000558334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001227267,"about_ca_topic_score_gemma":0.00002044268,"domain_scores_codex":[0.9974235,0.0002701706,0.0004336104,0.00100894,0.000447614,0.0004161587],"domain_scores_gemma":[0.9984435,0.0004077475,0.000277707,0.0005271224,0.0001771267,0.0001667413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006547001,0.0001443963,0.00002678918,0.00003102878,0.00001296994,0.000008022163,0.0001283056,0.002133612,0.003087943,0.0002104743,0.001089038,0.993062],"study_design_scores_gemma":[0.001354904,0.001410884,0.0004406002,0.00007248209,0.00001459232,0.00003539344,0.0000269576,0.9818029,0.002273897,0.003681319,0.008380494,0.0005056182],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02530602,0.00002770522,0.9706099,0.001269228,0.001299237,0.0009190722,0.00008767712,0.0003429143,0.0001382624],"genre_scores_gemma":[0.9494521,0.00004881073,0.04326938,0.005687979,0.0003352016,0.0009134705,0.000183709,0.00003940768,0.00006991756],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9925563,"threshold_uncertainty_score":0.9999049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0476342173372355,"score_gpt":0.2843524919410307,"score_spread":0.2367182746037952,"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."}}