{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000830445,0.001306079,0.0009938623,0.0005158908,0.000384097,0.0005939286,0.002115788,0.0009940545,0.002137253],"category_scores_gemma":[0.002221008,0.0006258747,0.0009336179,0.0005626018,0.001089286,0.001403013,0.00196511,0.00233794,0.0006893704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010157,"about_ca_system_score_gemma":0.0008266604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007453232,"about_ca_topic_score_gemma":0.006540452,"domain_scores_codex":[0.9994913,0.0000968184,0.0000163207,0.0001694171,0.000150449,0.00007563112],"domain_scores_gemma":[0.9990494,0.0004241173,0.0001078519,0.0001796302,0.0001613041,0.00007775321],"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":[0.00006713945,0.000023092,0.0004211932,0.00002074691,0.00002131736,0.00007009351,0.00002879742,0.9647866,0.001816539,0.003234904,0.001034273,0.02847531],"study_design_scores_gemma":[0.000002011221,0.000007520456,0.00003470608,0.000001268799,0.000001429395,0.000009859486,0.000001393463,0.9981024,0.0003925728,0.001339187,0.0001057343,0.000002090143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0159583,0.0001208999,0.9813437,0.0001557538,0.00003154116,0.00004743666,0.0001062006,0.001086115,0.001149886],"genre_scores_gemma":[0.8169193,0.0002320192,0.1746705,0.0003592875,0.00007314426,0.0002150605,0.0011334,0.0002888075,0.006108394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007453232,"threshold_uncertainty_score":0.01481968,"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."}}