{"id":"W4379116572","doi":"10.1109/lra.2023.3282381","title":"Use Your Imagination: A Detector-Independent Approach for LiDAR Quality Booster","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Point cloud; Lidar; Computer science; Inference; Artificial intelligence; RGB color model; Point (geometry); Block (permutation group theory); Detector; Process (computing); Computer vision; Remote sensing; Mathematics; Geography; 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":[0.0002247531,0.0001259302,0.0001264014,0.0001141264,0.0002099767,0.0002882121,0.000261619,0.00004055602,5.317074e-7],"category_scores_gemma":[0.00002750816,0.0001251432,0.00005141141,0.0004027547,0.00003596372,0.0007115268,0.00007265616,0.00008079498,0.00001432492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003910374,"about_ca_system_score_gemma":0.00001256008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003273764,"about_ca_topic_score_gemma":9.412864e-7,"domain_scores_codex":[0.9988728,0.00004433355,0.0002533105,0.0003716504,0.000222484,0.0002354165],"domain_scores_gemma":[0.9991872,0.0001845788,0.0001329227,0.0003531471,0.00007244707,0.00006967413],"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":[0.00001223499,0.0001069099,0.001281729,0.0001690846,0.00006026098,0.000007114373,0.001130492,0.7897727,0.07721618,0.06824747,0.009332055,0.05266376],"study_design_scores_gemma":[0.0002961299,0.00001494389,0.01367934,0.000006808252,0.000007636221,0.000006937879,0.0000121423,0.9821417,0.001448775,0.001369424,0.0008113409,0.0002048367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03610859,0.000009956007,0.9527117,0.01004819,0.0001959994,0.0004342052,0.000007924695,0.0004690961,0.00001435296],"genre_scores_gemma":[0.6462201,0.00001166857,0.3510615,0.002223265,0.0001357496,0.0001707125,0.00003457929,0.00001882249,0.0001235773],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6101115,"threshold_uncertainty_score":0.510319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06530455764265676,"score_gpt":0.3117415229898732,"score_spread":0.2464369653472165,"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."}}