{"id":"W4383108193","doi":"10.1109/icra48891.2023.10161226","title":"PCGen: Point Cloud Generator for LiDAR Simulation","year":2023,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Point cloud; Computer science; Lidar; Computer vision; Noise (video); Artificial intelligence; Scalability; Computer graphics (images); Real-time computing; Remote sensing","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001273702,0.00005152833,0.00004643699,0.00001606969,0.0001312208,0.00002002081,0.00005967583,0.00003056804,0.0003905727],"category_scores_gemma":[0.00002346003,0.00004480637,0.00003668616,0.0002134519,0.00002652195,0.00004266138,0.00003482765,0.0000229907,0.002771314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003497145,"about_ca_system_score_gemma":0.000002974189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005202621,"about_ca_topic_score_gemma":0.00002244949,"domain_scores_codex":[0.9995058,0.000009072882,0.00009139275,0.0001649474,0.00009381568,0.0001349824],"domain_scores_gemma":[0.9997022,0.00005790157,0.00001918011,0.0001683948,0.000004581834,0.00004778178],"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":[0.00002020108,0.00007336134,0.002885883,0.00001012246,0.00001782128,0.000001671605,0.0008726388,0.4698131,0.1291648,0.002879235,0.2697497,0.1245115],"study_design_scores_gemma":[0.0001735206,0.00002713582,0.008041943,0.000001476446,0.000006675451,7.928878e-7,0.00008136903,0.6054157,0.01316555,0.002680816,0.3702722,0.0001327603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8281822,0.000004509724,0.1273932,0.002279092,0.0002537617,0.0005283011,0.000009717865,0.0004466299,0.04090255],"genre_scores_gemma":[0.9842001,0.000001504959,0.007605884,0.0003159138,0.0001420829,0.000005768727,0.00002227323,0.00001116034,0.00769538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1560178,"threshold_uncertainty_score":0.9980052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0222620858665401,"score_gpt":0.2730689652420999,"score_spread":0.2508068793755598,"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."}}