{"id":"W4399574556","doi":"10.1109/3dv62453.2024.00147","title":"Stable Surface Regularization for Fast Few-Shot NeRF","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation","keywords":"Shot (pellet); Regularization (linguistics); Computer science; Materials science; Artificial intelligence","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":[0.001064411,0.001160216,0.001442854,0.001078758,0.0004015953,0.000985926,0.00162215,0.001596952,0.003893367],"category_scores_gemma":[0.002765492,0.0006135611,0.001291084,0.000695773,0.0006364847,0.001408716,0.001381186,0.002045903,0.002059369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532583,"about_ca_system_score_gemma":0.0007752214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002161997,"about_ca_topic_score_gemma":0.004730787,"domain_scores_codex":[0.9993539,0.000103375,0.00002961409,0.0001732538,0.0002742951,0.00006556223],"domain_scores_gemma":[0.9992257,0.0002772278,0.00006307851,0.0002171941,0.0001639284,0.00005280015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000243767,0.0001537274,0.001033341,0.0002694084,0.0001145738,0.0001858431,0.0002046805,0.2501698,0.05468955,0.01348341,0.01033349,0.6691184],"study_design_scores_gemma":[0.0000148352,0.00004935987,0.0001917136,0.00001164127,0.000008631312,0.0001585924,0.00002803093,0.9810309,0.00892904,0.006042399,0.003519304,0.00001550846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00538282,0.0001522105,0.9921005,0.00007445845,0.00003011054,0.00003251416,0.0001020834,0.001408376,0.0007168915],"genre_scores_gemma":[0.1274218,0.0002350731,0.8667845,0.0002064545,0.00005458332,0.0001237023,0.001503548,0.0007673939,0.002902929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003893367,"threshold_uncertainty_score":0.01302463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03842530962484668,"score_gpt":0.3453699052771041,"score_spread":0.3069445956522574,"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."}}