{"id":"W2940600945","doi":"10.1109/cvpr.2019.00605","title":"RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion","year":2019,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":206,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Reinforcement learning; Point cloud; Computer science; Noise (video); Cloud computing; Representation (politics); Dimension (graph theory); Artificial intelligence; Distributed computing; 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":[0.0006386948,0.0009142373,0.0006777326,0.000257246,0.0002407303,0.0004994869,0.001550703,0.0009744741,0.003473801],"category_scores_gemma":[0.001374802,0.0004125076,0.0006202328,0.0002292476,0.0006757163,0.0007210355,0.001112241,0.001853702,0.000917602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006557304,"about_ca_system_score_gemma":0.0007429035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003957066,"about_ca_topic_score_gemma":0.005862,"domain_scores_codex":[0.9997889,0.00004731774,0.000007496471,0.0000603914,0.00006484825,0.00003104973],"domain_scores_gemma":[0.9996675,0.0001439918,0.0000320275,0.00005924889,0.00006116359,0.00003605786],"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":[0.00008787864,0.00004892914,0.0005188404,0.00004502639,0.00003968199,0.0001145898,0.00003290299,0.919636,0.00511239,0.007377916,0.003677466,0.06330846],"study_design_scores_gemma":[0.000003257093,0.00001022641,0.00002416012,0.000002028067,0.000001495333,0.00001187062,0.000001047299,0.9980114,0.0005926776,0.0009306099,0.0004088841,0.000002356445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00645868,0.0001721624,0.9891861,0.0001434368,0.0000576199,0.00004423094,0.0000772382,0.001747911,0.002112684],"genre_scores_gemma":[0.5845935,0.0002828774,0.4038713,0.000471639,0.00008390284,0.000272478,0.0006687363,0.0006394532,0.009116122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003957066,"threshold_uncertainty_score":0.011621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015440442539984,"score_gpt":0.2126981788225495,"score_spread":0.2025437743971497,"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."}}