{"id":"W3003775208","doi":"10.1109/tip.2020.2969052","title":"Point Cloud Denoising via Feature Graph Laplacian Regularization","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Laplacian matrix; Artificial intelligence; Computer science; Noise reduction; Fidelity; Algorithm; Regularization (linguistics); Graph; Mathematics; Theoretical computer science","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":[0.0004656414,0.0006829571,0.0008548415,0.0007949997,0.0003200693,0.0006580242,0.001327104,0.001134716,0.001184987],"category_scores_gemma":[0.00172809,0.0003981512,0.0009796442,0.0009495224,0.0006357472,0.0008278649,0.001219138,0.001328527,0.0006593289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006753246,"about_ca_system_score_gemma":0.0009237969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004849237,"about_ca_topic_score_gemma":0.005236464,"domain_scores_codex":[0.9995779,0.00005241438,0.00001276345,0.00009934347,0.0002091461,0.00004843734],"domain_scores_gemma":[0.9995334,0.0001607687,0.00006082756,0.00007595923,0.0001343346,0.00003478778],"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":[0.0001077156,0.00006152262,0.0008487324,0.0001115787,0.00005893135,0.000179079,0.0001032387,0.8060203,0.03323729,0.02213092,0.005306443,0.1318343],"study_design_scores_gemma":[0.000003527501,0.000008043901,0.00008855886,0.000002473221,0.000002195284,0.00001894192,0.000004277661,0.9943696,0.001759201,0.003322389,0.0004168999,0.000003901822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005854934,0.00004666545,0.9931666,0.00008157683,0.00001276788,0.00001170823,0.00004005621,0.0002497899,0.0005358921],"genre_scores_gemma":[0.3187754,0.0004167415,0.674067,0.0002409925,0.00009471091,0.000186287,0.0007544732,0.0003646587,0.005099758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004849237,"threshold_uncertainty_score":0.009642005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133680422635431,"score_gpt":0.234045933363947,"score_spread":0.2227091291375927,"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."}}