{"id":"W3031288689","doi":"10.3390/rs12111729","title":"Review: Deep Learning on 3D Point Clouds","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":367,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Artificial intelligence; Deep learning; Raw data; Segmentation; Representation (politics); Point (geometry); Machine learning; Data mining; Mathematics","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":[0.0009482242,0.0009355788,0.000963668,0.001860552,0.0002354876,0.001223661,0.001787087,0.001519608,0.004728955],"category_scores_gemma":[0.004647719,0.0004709153,0.0007450762,0.00278674,0.000539994,0.002423238,0.0008581509,0.002020878,0.004183962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009017762,"about_ca_system_score_gemma":0.001613196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003800015,"about_ca_topic_score_gemma":0.002889416,"domain_scores_codex":[0.9995197,0.00009728337,0.00005091787,0.00009640376,0.0002019582,0.00003382897],"domain_scores_gemma":[0.9981105,0.0008383753,0.0001092019,0.0000894458,0.0007812409,0.00007118283],"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":[0.00006461015,0.00004186052,0.0003971236,0.009447287,0.0001829048,0.00009192094,0.00003703847,0.00613046,0.0006790716,0.01052921,0.2038565,0.768542],"study_design_scores_gemma":[0.00002069822,0.00008710014,0.0009062317,0.004306269,0.0001306794,0.0004422804,0.00003959469,0.005107907,0.001161352,0.009682668,0.9780623,0.00005279078],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000727372,0.9656482,0.01898536,0.004951051,0.002849235,0.00004583043,0.0005093074,0.0002684334,0.006015266],"genre_scores_gemma":[0.006346245,0.9784012,0.005510828,0.002795801,0.002114238,0.00005739733,0.0009682989,0.00009708073,0.003708989],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004728955,"threshold_uncertainty_score":0.01581991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618435042585307,"score_gpt":0.2239378541802312,"score_spread":0.2077535037543781,"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."}}