{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003487271,0.001262355,0.0007230148,0.0008235662,0.0003374078,0.0006519809,0.002186398,0.0009056919,0.003982966],"category_scores_gemma":[0.001166588,0.0005392031,0.0005842335,0.0007221995,0.0003848413,0.001074896,0.001091253,0.001004804,0.001743911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009849933,"about_ca_system_score_gemma":0.0007485196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01474452,"about_ca_topic_score_gemma":0.01931746,"domain_scores_codex":[0.9998441,0.00001045928,0.000005651896,0.00006073527,0.00004986893,0.00002929911],"domain_scores_gemma":[0.9997825,0.00003347599,0.00001807078,0.00006509059,0.00008003691,0.00002075268],"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":[0.0003225947,0.0001963588,0.002976303,0.0001594741,0.0001404337,0.0001970392,0.00010327,0.3668911,0.02651624,0.006531878,0.04152576,0.5544395],"study_design_scores_gemma":[0.00001310072,0.0000293622,0.0002518162,0.000006363892,0.00001114329,0.00003095431,0.00000926462,0.9894192,0.005918925,0.001875298,0.002426887,0.000007510506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0875624,0.0007919982,0.8698292,0.0006308001,0.0003907702,0.0001938591,0.002452233,0.03134065,0.006808227],"genre_scores_gemma":[0.4881967,0.0005808921,0.4819979,0.0006888031,0.0001420755,0.0003087093,0.009459211,0.001169885,0.0174558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01474452,"threshold_uncertainty_score":0.02931738,"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."}}