{"id":"W3197310284","doi":"10.1109/tgrs.2021.3105551","title":"Dense Point Cloud Completion Based on Generative Adversarial Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Feature (linguistics); Ground truth; Discriminator; Artificial intelligence; Point (geometry); Cloud computing; Encoder; Data mining; Pattern recognition (psychology); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001520734,0.0001331342,0.0001537379,0.00008723673,0.0004146963,0.00006810494,0.0000338398,0.00006323726,0.00001431387],"category_scores_gemma":[0.000005009833,0.0001277327,0.0000858407,0.0003810262,0.00006012565,0.00005869118,6.562317e-7,0.0002014395,0.00001250083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004889159,"about_ca_system_score_gemma":0.00003279945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000659151,"about_ca_topic_score_gemma":0.0001207364,"domain_scores_codex":[0.9991143,0.00005398375,0.0001483035,0.0002619906,0.000190472,0.0002309338],"domain_scores_gemma":[0.9996139,0.00006588694,0.00001796204,0.0001641273,0.00005819662,0.00007995893],"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":[0.000009486048,0.000008632343,1.631935e-7,0.00000517387,0.0000116047,0.00001610412,0.0001031341,0.906792,0.003712308,0.000001881701,0.00004435819,0.08929512],"study_design_scores_gemma":[0.0002120885,0.0000320027,0.00001392954,0.00008807822,0.00003908768,0.00001822362,0.00007684848,0.9878747,0.01129607,0.00008626908,0.0001135033,0.0001491282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05572026,0.00002985712,0.9425249,0.0002541025,0.00101494,0.00003945835,0.000005432872,0.0001084131,0.0003026517],"genre_scores_gemma":[0.9503588,0.00006854004,0.04885471,0.0003861183,0.0001909443,6.613653e-8,0.000003245255,0.0000135784,0.0001239836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8946385,"threshold_uncertainty_score":0.520879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392529224786294,"score_gpt":0.2155700407601038,"score_spread":0.2016447485122408,"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."}}