{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005389574,0.001623897,0.0007851308,0.0008638712,0.0003980248,0.0011611,0.00278605,0.001143882,0.02031439],"category_scores_gemma":[0.002492125,0.0007806925,0.001145119,0.0007430015,0.0004528923,0.001030049,0.001671898,0.001829948,0.006740841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005468597,"about_ca_system_score_gemma":0.001199614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00432271,"about_ca_topic_score_gemma":0.005389102,"domain_scores_codex":[0.9996256,0.00004355128,0.00002694584,0.00008336235,0.0001752665,0.00004527624],"domain_scores_gemma":[0.9995137,0.000141078,0.00002440166,0.0001359468,0.0001341166,0.00005062033],"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":[0.0009644183,0.0003275111,0.007511983,0.001116718,0.0002803796,0.0008870668,0.0005352078,0.5722749,0.0196137,0.0180461,0.1788584,0.1995836],"study_design_scores_gemma":[0.000116401,0.000042193,0.0004560897,0.00002826694,0.00001274554,0.00008570881,0.0000373892,0.9607084,0.01008394,0.004760763,0.02364137,0.00002676924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02111793,0.0004301314,0.7506385,0.0003952617,0.0003998953,0.0005823322,0.01299452,0.2040611,0.009380291],"genre_scores_gemma":[0.3321835,0.0008831778,0.5857162,0.0005868889,0.000102737,0.0019326,0.04285888,0.02680563,0.008930491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02031439,"threshold_uncertainty_score":0.06795841,"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."}}