{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007063409,0.0007359929,0.0006611345,0.0008213131,0.0002322001,0.0006227096,0.0008987504,0.0009006968,0.0004279846],"category_scores_gemma":[0.001073319,0.0004373263,0.0007618313,0.0006884129,0.0005426779,0.001335308,0.001263981,0.0006958191,0.0002144755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004257772,"about_ca_system_score_gemma":0.0005261964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003443301,"about_ca_topic_score_gemma":0.00373043,"domain_scores_codex":[0.9997116,0.00004233101,0.00001206045,0.00009743781,0.00008130759,0.0000552827],"domain_scores_gemma":[0.9997528,0.00006586878,0.00006425836,0.00003640809,0.00006132587,0.0000192544],"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":[0.0002822137,0.0001847791,0.006213014,0.00009105063,0.0001141791,0.0002289332,0.0001671873,0.4670956,0.1186876,0.004603078,0.001174801,0.4011576],"study_design_scores_gemma":[0.000003395366,0.00002813007,0.0009809361,0.000003076777,0.00001053252,0.00003483217,0.00001148761,0.9909524,0.006121228,0.00158196,0.0002623871,0.000009575185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08895847,0.0002765104,0.9090694,0.0001383175,0.00002886767,0.00003656706,0.00007042726,0.0007423259,0.0006790862],"genre_scores_gemma":[0.8248869,0.0002524346,0.1732758,0.0001723687,0.00004537308,0.00005103894,0.0002021199,0.00006135184,0.001052664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003443301,"threshold_uncertainty_score":0.006846488,"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."}}