{"id":"W4402727857","doi":"10.1109/cvpr52733.2024.01436","title":"Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; National Research Foundation of Korea; Ministry of Trade, Industry and Energy","keywords":"Lidar; Computer science; Fusion; Remote sensing; Radar; Radar imaging; Object (grammar); Sensor fusion; Computer vision; Artificial intelligence; Geology; Telecommunications","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":[0.00007282877,0.0001068126,0.00008749777,0.0001245382,0.0001434168,0.0001691615,0.0001574292,0.00004184935,0.00002594008],"category_scores_gemma":[0.0000043825,0.00008239369,0.00001630766,0.0007614013,0.00005283769,0.0004066316,0.00009718699,0.0001538269,0.00002073403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005381941,"about_ca_system_score_gemma":0.00005079217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009131132,"about_ca_topic_score_gemma":0.0005488959,"domain_scores_codex":[0.9991886,0.0000251644,0.0001183664,0.0003788492,0.0001240448,0.0001649819],"domain_scores_gemma":[0.9995799,0.00006699053,0.00001938286,0.0002585371,0.00002263515,0.00005256907],"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":[0.00002597038,0.0001738664,0.0003702181,0.0000610096,0.0000450333,0.0001823212,0.002061153,0.01591953,0.02701312,0.26216,0.0005084584,0.6914793],"study_design_scores_gemma":[0.0007751223,0.0004614551,0.01211677,0.0001681411,0.00002901332,0.0005668035,0.0001003532,0.9108173,0.01200997,0.04429343,0.01795385,0.0007077778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02623354,0.0002000188,0.9694584,0.0007077049,0.00009126017,0.0002457373,0.000002252825,0.0003802519,0.002680861],"genre_scores_gemma":[0.8610809,0.00005022249,0.1382978,0.0001516029,0.00004104162,0.0000655067,0.000002309457,0.00001192236,0.000298739],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8948978,"threshold_uncertainty_score":0.3359917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123740202532702,"score_gpt":0.2418491685329306,"score_spread":0.2306117665076036,"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."}}