{"id":"W4328029556","doi":"10.1111/cgf.14661","title":"MODNet: Multi‐offset Point Cloud Denoising Network Customized for Multi‐scale Patches","year":2022,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province","keywords":"Computer science; Point cloud; Offset (computer science); Cloud computing; Scale (ratio); Artificial intelligence; Noise reduction; Computer graphics (images); Computer vision; Cartography; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004265688,0.0002693531,0.0004001012,0.0001997692,0.0005897754,0.00007738397,0.0003522288,0.00008948877,0.00001425023],"category_scores_gemma":[0.000005251789,0.000292729,0.0004314526,0.0004507255,0.00003300325,0.00008276049,0.0002243482,0.000323605,0.000005443562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006829689,"about_ca_system_score_gemma":0.0000167977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000268756,"about_ca_topic_score_gemma":0.0000900275,"domain_scores_codex":[0.9983594,0.00006323687,0.0003945136,0.000357128,0.0002066395,0.0006191023],"domain_scores_gemma":[0.9992837,0.0001007031,0.0000660365,0.0003676002,0.00005899009,0.0001230236],"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":[0.00002018169,0.00006419785,0.001295815,0.00003720688,0.0001759036,0.000004028581,0.0003647917,0.9750088,0.0000603085,0.0002801453,0.01883297,0.003855662],"study_design_scores_gemma":[0.001462193,0.00004183313,0.00006081185,0.00002286873,0.00007362173,0.000005287785,0.0001074624,0.9901559,0.00005852294,0.001119876,0.006531,0.0003606598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04430512,0.0009666482,0.9521853,0.0001900587,0.001485959,0.0002836724,0.00007858049,0.0004949454,0.000009748365],"genre_scores_gemma":[0.9101683,0.00006700608,0.08818871,0.0005699227,0.0005060094,0.0001457099,0.0001791849,0.0001031337,0.00007196116],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8658633,"threshold_uncertainty_score":0.9999525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310367042294361,"score_gpt":0.2307911415100938,"score_spread":0.2076874710871501,"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."}}