{"id":"W3080980548","doi":"10.1109/tnnls.2020.3015992","title":"Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review","year":2020,"lang":"en","type":"review","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":574,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Lidar; Computer science; Deep learning; Artificial intelligence; Segmentation; Object detection; Discriminative model; Field (mathematics); Milestone; Feature (linguistics); Key (lock); Point (geometry); Task (project management); Machine learning; Data science; Remote sensing; Systems engineering; Engineering; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008071139,0.001001774,0.0008186279,0.001883898,0.0002048703,0.0009147619,0.001138034,0.001070471,0.003230265],"category_scores_gemma":[0.001768057,0.0005219715,0.0006300473,0.002879216,0.000369658,0.00201071,0.0008586199,0.001290428,0.002075043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005023952,"about_ca_system_score_gemma":0.001314755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002376938,"about_ca_topic_score_gemma":0.002314747,"domain_scores_codex":[0.9997715,0.00003327079,0.00002882286,0.00005826711,0.00008745044,0.00002062828],"domain_scores_gemma":[0.9992766,0.0004007201,0.00005044887,0.00002418327,0.0002176563,0.00003033742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004248902,0.00005877738,0.0004195296,0.008805961,0.00008778299,0.0000619253,0.00004380483,0.00265254,0.0007612034,0.006806336,0.01777053,0.9624891],"study_design_scores_gemma":[0.00001913725,0.000257868,0.001598447,0.007329231,0.0003516057,0.0007890633,0.0001097641,0.006978943,0.00264171,0.00906976,0.9707845,0.00006998781],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005717334,0.991227,0.004835883,0.0004795594,0.0002614423,0.0000134934,0.00006744036,0.00004358157,0.002499913],"genre_scores_gemma":[0.00401063,0.9914759,0.002713864,0.0002567727,0.000271278,0.00001572367,0.0001347652,0.00001329505,0.001107768],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003230265,"threshold_uncertainty_score":0.01080632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02805834395490626,"score_gpt":0.2917329016432967,"score_spread":0.2636745576883904,"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."}}